Reviewerly/exHarmony huggingface dataset:
An Imitation Learning Approach to Unsupervised Parsing

Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning.Unfortunately, the learned trees often do not match actual syntax trees well.Shen et al. (2018) propose a structured attention mechanism for language modeling (PRPN), which induces better syntactic structures but relies on ad hoc heuristics.Also, their model lacks interpretability as it is not grounded in parsing actions.In our work, we propose an imitation learning approach to unsupervised parsing, where we transfer the syntactic knowledge induced by the PRPN to a Tree-LSTM model with discrete parsing actions.Its policy is then refined by Gumbel-Softmax training towards a semantically oriented objective.We evaluate our approach on the All Natural Language Inference dataset and show that it achieves a new state of the art in terms of parsing F -score, outperforming our base models, including the PRPN. 1
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections

Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages.However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available.In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly.The parameters of source model and target model are softly shared through a regularized log likelihood objective.An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect crosslingual word embedding input.We evaluate our method on two syntactic tasks: part-ofspeech (POS) tagging and dependency parsing.On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages.On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models. 1 3 Following Ahmad et al. ( 2019), we use the offline pre-trained alignment matrix present in https://github.com/Babylonpartners/fastText_multilingual, which contains alignment matrices for 78 languages, which also allows comparison with their numbers in Section 4.3.
Sparse Label Smoothing Regularization for Person Re-Identification

Person re-identification (re-id) is a cross-camera retrieval task which establishes a correspondence between images of a person from multiple cameras. Deep learning methods have been successfully applied to this problem and have achieved impressive results. However, these methods require a large amount of labeled training data. Currently, the labeled datasets in person re-id are limited in their scale and manual acquisition of such large-scale datasets from surveillance cameras is a tedious and labor-intensive task. In this paper, we propose a framework that performs intelligent data augmentation and assigns the partial smoothing label to generated data. Our approach first exploits the clustering property of existing person re-id datasets to create groups of similar objects that model cross-view variations. Each group is then used to generate realistic images through adversarial training. Our aim is to emphasize the feature similarity between generated samples and the original samples. Finally, we assign a non-uniform label distribution to the generated samples and define a regularized loss function for training. The proposed approach tackles two problems 1) how to efficiently use the generated data and 2) how to address the over-smoothness problem found in current regularization methods. The extensive experiments on four large-scale datasets show that our regularization method significantly improves the re-id accuracy compared to existing methods.
The Effects of Watching a Virtual Reality (VR) Forest Video on Stress Reduction in Adults

This study was conducted to investigate the effects of watching a virtual reality (VR) forest video on stress reduction in adults.Experiments were performed in an artificial climate chamber where temperature, humidity and illumination were controlled.To cause stress in subjects, the subjects were asked to multiply two digit numbers by one digit numbers for two minutes.The subjects then watched a VR forest video for five minutes.During the experiment, the heart rate variability (HRV) and heart rate (HR) of subjects were continuously measured for evaluating their physiological state.After measuring the baseline and watching a VR forest video, their psychological state was evaluated using the profile of mood state (POMS), semantic differential (SD) method, and positive affect and negative affect schedule (PANAS).The results of physiological evaluation after watching a VR forest video showed a decrease in the stress index and HR, an increase in the activity of the parasympathetic nervous system, and the standard deviation of normal to normal RR intervals (SDNN).The results of psychological assessment confirmed that watching a VR forest video induced "comfort", "natural", and "soothed" feelings in subjects.In addition, watching a VR forest video decreased "tension-anxiety (T-A)", "depression-dejection (D)", "anger-hostility (A-H)", "fatigue (F)", "total mood disturbance (TMD)", and increased "vigor (V)" compared to the base line, and decreased "negative affect" and increased "positive affect."These results indicate that watching a VR forest video decreases adults' stress index, stabilizes physiological state, and has a positive impact on psychological state.The findings of this study can serve as a basis for providing forest welfare services to people in the blind spots of forest welfare, and can lay a foundation for the adoption of VR, one of the technologies of the fourth industrial revolution in forestry.
Differentially Private Confidence Intervals for Empirical Risk Minimization

The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the problem of designing confidence intervals for the parameters of a variety of differentially private machine learning models. The algorithms can provide confidence intervals that satisfy differential privacy (as well as the more recently proposed concentrated differential privacy) and can be used with existing differentially private mechanisms that train models using objective perturbation and output perturbation.
Learning to Rank for Plausible Plausibility

Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks.Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis).The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training.We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context.Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss.Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI.We find that a margin-based loss leads to a more plausible model of plausibility.Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss.
LKAQ: Large-scale knowledge graph approximate query algorithm

The problems of storing and processing queries for knowledge graphs (KGs) have always been a hot topic in the database community. Various tools, for example, 3store, RDF-3X, Jena2, and gStore, have been proposed. Recently, KGs have gradually shown a non-strict structure, and their volumes continue to grow. As a result, current KG storage and query tools cannot handle the intricate relationships in KGs or support massive data in limited memory space. In addition, an increasing number of users want to use KGs under limited computing resources. Therefore, to meet the current needs of KGs and solve the above problems, we propose a large-scale knowledge graph approximate query algorithm (LKAQ) adopting the idea of an approximate query processing algorithm. LKAQ gives users the ability to control the trade-off among query time, accuracy, and in-memory usage. From extensive experiments, we demonstrate that LKAQ outperforms state-of-the-art approaches with memory constraints.
On the Estimation of Treatment Effect with Text Covariates

Estimating the treatment effect benefits decision making in various domains as it can provide the potential outcomes of different choices. Existing work mainly focuses on covariates with numerical values, while how to handle covariates with textual information for treatment effect estimation is still an open question. One major challenge is how to filter out the nearly instrumental variables which are the variables more predictive to the treatment than the outcome. Conditioning on those variables to estimate the treatment effect would amplify the estimation bias. To address this challenge, we propose a conditional treatment-adversarial learning based matching method (CTAM). CTAM incorporates the treatment-adversarial learning to filter out the information related to nearly instrumental variables when learning the representations, and then it performs matching among the learned representations to estimate the treatment effects. The conditional treatment-adversarial learning helps reduce the bias of treatment effect estimation, which is demonstrated by our experimental results on both semi-synthetic and real-world datasets.
The kinematic source analysis for 2018 Mw 6.4 Hualien, Taiwan earthquake

The 2018 M w 6.4 Hualien earthquake struck the eastern Taiwan and caused serious damage.We investigate the rupture properties of the 2018 Hualien earthquake by inverting teleseismic body wave and forward modeling GPS coseismic deformation.The rupture process and slip pattern of preferred model explain both the far-field (teleseismic data) and near-field (GPS) observations.The results show that the 2018 Hualien mainshock ruptured southward on two fault segments, with a weak but fast initiation (3.0 km s -1 ) in the main west-dipping segment F1 and slow (2.0 km s -1 ) yet significant slip on shallow east-dipping segment F2.In the past few years, several moderate-sized events, which struck eastern Taiwan and caused strong ground shaking and some seismic damage, are considered occurring on the west-dipping fault.Additional investigations are required to building up the knowledge of this not wellknown region.
Multiple Noisy Label Distribution Propagation for Crowdsourcing

Crowdsourcing services provide a fast, efficient, and cost-effective means of obtaining large labeled data for supervised learning. Ground truth inference, also called label integration, designs proper aggregation strategies to infer the unknown true label of each instance from the multiple noisy label set provided by ordinary crowd workers. However, to the best of our knowledge, nearly all existing label integration methods focus solely on the multiple noisy label set itself of the individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this study. MNLDP first transforms the multiple noisy label set of each instance into its multiple noisy label distribution and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Promising experimental results on simulated and real-world datasets validate the effectiveness of our proposed method.
Multi-View Active Learning for Video Recommendation

On many video websites, the recommendation is implemented as a prediction problem of video-user pairs, where the videos are represented by text features extracted from the metadata. However, the metadata is manually annotated by users and is usually missing for online videos. To train an effective recommender system with lower annotation cost, we propose an active learning approach to fully exploit the visual view of videos, while querying as few annotations as possible from the text view. On one hand, a joint model is proposed to learn the mapping from visual view to text view by simultaneously aligning the two views and minimizing the classification loss. On the other hand, a novel strategy based on prediction inconsistency and watching frequency is proposed to actively select the most important videos for metadata querying. Experiments on both classification datasets and real video recommendation tasks validate that the proposed approach can significantly reduce the annotation cost.
Finding Optimal Solutions in HTN Planning - A SAT-based Approach

Over the last years, several new approaches to Hierarchical Task Network (HTN) planning have been proposed that increased the overall performance of HTN planners. However, the focus has been on agile planning - on finding a solution as quickly as possible. Little work has been done on finding optimal plans. We show how the currently best-performing approach to HTN planning - the translation into propositional logic - can be utilised to find optimal plans. Such SAT-based planners usually bound the HTN problem to a certain depth of decomposition and then translate the problem into a propositional formula. To generate optimal plans, the length of the solution has to be bounded instead of the decomposition depth. We show the relationship between these bounds and how it can be handled algorithmically. Based on this, we propose an optimal SAT-based HTN planner and show that it performs favourably on a benchmark set.
Efficient Multiparty Quantum Key Agreement With a Single $d$ -Level Quantum System Secure Against Collusive Attack

Recently, multiparty quantum key agreement (MQKA) protocols have gained increasing attention.Several MQKA protocols in the traveling mode have been proposed for higher key generation rates, which, however, are vulnerable to a collusive attack presented by Liu.In this paper, an analysis of Liu's collusive attacks on Cao et al.'s protocol is given, based on which an efficient MQKA protocol that requires only sequential communication of a single d-level quantum system is put forward.Because of the single d-level quantum system, our protocol has huge advantages in scalability and can be realized with the state-of-the-art technology.The scalability means that each additional participant is only required to perform a unitary operator that is comparatively simple, and the complexity of measurements remains independent of the number of participants.Our protocol can also resist Liu's collusive attacks due to the encrypted quantum channel and has an efficiency rate of 1 2N (N is the number of participants) that is much better than those of other quantum key agreement protocols.In addition, by using the proposed encoding method, a wide class of problems for which quantum information protocols based on multisystem entanglement can be mapped into much simpler ones involving only one system.
Coreference Aware Representation Learning for Neural Named Entity Recognition

Recent neural network models have achieved state-of-the-art performance on the task of named entity recognition (NER). However, previous neural network models typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases or entities. In this paper, we propose a novel approach to learn coreference-aware word representations for the NER task at the document level. In particular, we enrich the well-known neural architecture ``CNN-BiLSTM-CRF'' with a coreference layer on top of the BiLSTM layer to incorporate coreferential relations. Furthermore, we introduce the coreference regularization to ensure the coreferential entities to share similar representations and consistent predictions within the same coreference cluster. Our proposed model achieves new state-of-the-art performance on two NER benchmarks: CoNLL-2003 and OntoNotes v5.0. More importantly, we demonstrate that our framework does not rely on gold coreference knowledge, and can still work well even when the coreferential relations are generated by a third-party toolkit.
Towards an Ontology Design Pattern for UAV Video Content Analysis

Video scene understanding is leading to an increased research investment in developing artificial intelligence technologies, pattern recognition, and computer vision, especially with the advance in sensor technologies. Developing autonomous unmanned vehicles, able to recognize not just targets appearing in a scene but a complete scene the targets are involved in (describing events, actions, situations, etc.) is becoming crucial in the recent advanced intelligent surveillance systems. At the same time, besides these consolidated technologies, the Semantic Web Technologies are also emerging, yielding seamless support to the high-level understanding of the scenes. To this purpose, the paper proposes a systematic ontology modeling to support and improve video content analysis, by generating a comprehensive high-level scene description, achieved by semantic reasoning and querying. The ontology schema comes from as an integration of new and existing ontologies and provides some design pattern guideline to get a high-level description of a whole scenario. It starts from the description of basic targets in the video scenario, thanks to the support of video tracking algorithms and target classification; then provides a higher level interpretation, compounding event-driven target interactions (for local activity comprehension), to reach gradually an abstraction high level that enables a concise and complete scenario description.
RecoNet: An Interpretable Neural Architecture for Recommender Systems

Neural systems offer high predictive accuracy but are plagued by long training times and low interpretability. We present a simple neural architecture for recommender systems that lifts several of these shortcomings. Firstly, the approach has a high predictive power that is comparable to state-of-the-art recommender approaches. Secondly, owing to its simplicity, the trained model can be interpreted easily because it provides the individual contribution of each input feature to the decision. Our method is three orders of magnitude faster than general-purpose explanatory approaches, such as LIME. Finally, thanks to its design, our architecture addresses cold-start issues, and therefore the model does not require retraining in the presence of new users.
WiRe57 : A Fine-Grained Benchmark for Open Information Extraction

We build a reference for the task of Open Information Extraction, on five documents.We tentatively resolve a number of issues that arise, including coreference and granularity, and we take steps toward addressing inference, a significant problem.We seek to better pinpoint the requirements for the task.We produce our annotation guidelines specifying what is correct to extract and what is not.In turn, we use this reference to score existing Open IE systems.We address the nontrivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script.Among seven compared extractors, we find the MinIE system to perform best.
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both researchers and practitioners in reproducing the results for further comparisons. Although a portion of papers provides source code, they adopted different programming languages or different deep learning packages, which also raises the bar in grasping the ideas. To alleviate this problem, we released the open source project: \textbf{DeepRec}. In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow. Three major recommendation scenarios: rating prediction, top-N recommendation (item ranking) and sequential recommendation, were considered. Meanwhile, DeepRec maintains good modularity and extensibility to easily incorporate new models into the framework. It is distributed under the terms of the GNU General Public License. The source code is available at github: https://github.com/cheungdaven/DeepRec
Impact of reading on students’ writing ability

For college students, reading and writing are very necessary for the success of their studies.Reading habit can severe their knowledge and comprehension in their field of subjects.This study sets out to investigate how students' reading skills affect students' academic writing.Fifty-two master students of English Language Education at Universitas Ahmad Dahlan in Yogyakarta, Indonesia were involved in this correlational study.Two questionnaires were used as instruments to obtain the data, i.e., the reading habit survey and the academic writing skill survey.The data were analyzed with descriptive statistics and single linear regression tests along with the prerequisite test of normality.The results revealed that the reading habit and writing skills of the students were at the medium level.Meanwhile, the other result showed that the reading habit had a profound effect on college students' academic writing skill shown by the p-value of T-test = 0.000 and F score = 352,524 at the significant 0,000 ≤ 0,05.In other words, reading habit has a significant influence on college students' academic writing skill.
Implementation of Fuzzy Functions Aimed at Fairer Grading of Students’ Tests

The main goal of this research is to achieve fairer assessment of students’ knowledge and skills in tests in view of the fact that learners with similar abilities are often assigned different test grades if their achievements are borderline cases between two grades. As an example, a student who has scored 29 points out of 60 in a test will fail, while another one with 30 or 31 points will pass, which can be viewed as unjust. This problem is addressed by using fuzzy logic and different fuzzy functions to determine whether students with borderline test scores are assigned the higher or the lower grade of the two. Furthermore, although changing the grades of individual students, the authors endeavor to preserve the overall score of all test takers statistically unchanged.