Reviewerly/exHarmony huggingface dataset:
Exploring Changes in Coastal Environment Policy Using Text Mining: A Case Study in South Korea

Cho, N.W. and Lee, M.J., 2020. Exploring changes in coastal environment policy using text mining: A case study in South Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 47-53. Coconut Creek (Florida), ISSN 0749-0208.Land development activities performed in coastal areas are accompanied by environmental impacts, and Environmental Impact Assessment (EIA) is used to mitigate such impacts. The characteristics of coastal environmental policy can be determined based on the content of EIA reports for development projects However, owing to the vast amounts of information regarding various environmental factors, including air, water, and soil, contained in such reports, their potential for analysis is limited. This study analyzed the terms of agreement for development projects—the result of EIA of coastal areas—using bibliometrics, a method that is widely used to analyze trends in academic research. Data on the EIA of coastal development projects conducted in South Korea over the past 25 years were collected, and the surveyed period was sub-divided into five-year periods to build datasets through text mining. Subsequently, trends in coastal environmental policy were analyzed over an extended period. The results of the analysis indicated that keywords related to the water environment showed the highest appearance frequency for the entire period due to the characteristics of coastal development projects. Keywords related to the natural ecological environment continued to show an increasing trend, while those related to the land environment showed a continuously decreasing trend. Based on correspondence analysis, the five sub-divided periods were broadly classified into three groups, and the principal inertia of the analysis was found to be approximately 89.6 %. The characteristics of the coastal environmental policy that were emphasized for each period were determined; land and life environments were found to be emphasized for the period between 1994 and 1999; water environments were emphasized between 2000 and 2009; and natural ecology environments were emphasized between 2010 and 2019. By contrast, atmospheric and socio-economic environments did not show a significant correlation. This study proposes methods of utilizing information provided by EIA reports to analyze environmental policy and offers new insights specifically applicable to coastal environmental policy.
Attention-guided aggregation stereo matching network

Existing stereo matching networks based on deep learning lack multi-level and multi-module attention and integration for feature information. Therefore, we propose an attention-guided aggregation stereo matching network to encode and integrate information multiple times. Specifically, we design a residual network based on the 2D channel attention block to adaptively calibrate weight response, improving the robustness of the feature representation. We also construct a 3D stacked hourglass structure based on the 3D channel attention block to calibrate the weight response of the 4D cost volume in the channel dimension, further enhancing the network guidance and aggregation capabilities. In addition, we introduce a 4D guided cost volume, which pre-groups the extracted image features and exploits the similarity measures in each group to guide the concatenation features, further realizing interactive learning of cost volume. The experimental results on the Scene Flow and KITTI benchmark datasets showed that the proposed network significantly improves the prediction disparity accuracy with a small increase in calculation time.
Connectedness criteria for graphs by means of omega invariant

A realizable degree sequence can be realized in many ways as a graph. There are several tests for determining realizability of a degree sequence. Up to now, not much was known about the common properties of these realizations. Euler characteristic is a well-known characteristic of graphs and their underlying surfaces. It is used to determine several combinatorial properties of a surface and of all graphs embedded onto it. Recently, last two authors defined a number ? which is invariant for all realizations of a given degree sequence. ? is shown to be related to Euler characteristic and cyclomatic number. Several properties of ? are obtained and some applications in extremal graph theory are done by authors. As already shown, the number gives direct information compared with the Euler characteristic on the realizability, number of realizations, being acyclic or cyclic, number of components, chords, loops, pendant edges, faces, bridges etc. In this paper, another important topological property of graphs which is connectedness is studied by means of ?. It is shown that all graphs with ?(G)?-4 are disconnected, and if ?(G)? -2, then the graph could be connected or disconnected. It is also shown that if the realization is a connected graph and ?(G)=-2, then certainly the graph should be acyclic. Similarly, it is shown that if the realization is a connected graph G and ?(G)? 0, then certainly the graph should be cyclic. Also, the fact that when ?(G)?-4, the components of the disconnected graph could not all be cyclic, and that if all the components of a graph G are cyclic, then ?(G) ? 0 are proven.
VDARN: Video Disentangling Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition

In most supervised action recognition methods, sufficient labeled training instances are needed for each class, the learned model can only recognize the samples belonging to classes covered by the training data, and lacks the ability to deal with previously unseen classes. In this paper, we tackle the above challenges by proposing a novel Video Disentangling Attentive Relation Network (VDARN), which can be trained in an end-to-end manner by using a standard deep neural network framework. Compared with most of few-shot Action Recognition (FSAR) and Zero-Shot Action Recognition (ZSAR) methods, the proposed method considers the natural temporal misalignment between object cues and human motion cues in the visual space. Specifically, we propose a video disentangling module to decompose segment-wise video into motion cues and object cues. Furthermore, we design a dual branch attention module to align the segment-wise motion cues and segment-wise object cues in the temporal domain by learning the temporal autocorrelation (in the case of FSAR) of the two cues. In the case of ZSAR, the dual branch attention module is used to estimates the similarities between the semantic embedding and segment-wise motion/object cues of the video. Finally, a relation module is proposed to learn and measure the distance or similarity of video–video (FSAR) or video-unseen label (ZSAR). Extensive experimental results on three realistic action benchmark Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework in both few-shot action recognition and zero-shot action recognition.
Augmented Reality Lights for Compromised Visibility Navigation

This paper considers the feasibility of using augmented reality (AR) as a tool for enhancing visualization in maritime operations to avoid collision in different environmental conditions. According to the International Maritime Organization (IMO 2010), 90% of maritime accidents due to collisions at sea are caused in part by human error. This study investigates the new technology (AR) used to superimpose holographic images onto the real world; now reaching a state of readiness for commercial application. This paper demonstrates the competence of AR technology to serve as a maritime navigational aid. The research explores the viability of improving navigational safety in low visibility by projecting holograms of real-world objects in the same geo-location as the real object to make them “visible”. The paper presents the logical deconstruction of the technical problems and identified solutions, together with results of experiments used to validate the concept and technology readiness for real word maritime application. The paper presents a verified demonstrator; a proposed holographic bridge interface with an innovative way of presenting information using AR technology. Furthermore, it identifies that new technologies offer the opportunity for enhanced operator performances, with the expectation being that this should lead to reduce risk to persons, property, and the environment.
Web Crawler: Design And Implementation For Extracting Article-Like Contents

The World Wide Web is a large, wealthy, and accessible information system whose users are increasing rapidly nowadays. To retrieve information from the web as per users’ requests, search engines are built to access web pages. As search engine systems play a significant role in cybernetics, telecommunication, and physics, many efforts were made to enhance their capacity.However, most of the data contained on the web are unmanaged, making it impossible to access the entire network at once by current search engine system mechanisms. Web Crawler, therefore, is a critical part of search engines to navigate and download full texts of the web pages. Web crawlers may also be applied to detect missing links and for community detection in complex networks and cybernetic systems. However, template-based crawling techniques could not handle the layout diversity of objects from web pages. In this paper, a web crawler module was designed and implemented, attempted to extract article-like contents from 495 websites. It uses a machine learning approach with visual cues, trivial HTML, and text-based features to filter out clutters. The outcomes are promising for extracting article-like contents from websites, contributing to the search engine systems development and future research gears towards proposing higher performance systems.
Real-Time Model-Based Monocular Pose Tracking for an Asteroid by Contour Fitting

This article presents an innovative framework of real-time vision-based pose tracking for asteroid using the contour information of the asteroid image. At the first-time instant, the tracking is initialized by the distance-based template matching and contour-based pose optimization. Subsequently, at each time instant, with the prediction of the extended Kalman filter (EKF) as initial guess, the pose of the asteroid is obtained in real time by geometrically fitting the contour of the projected asteroid CAD model over the contour of the asteroid image with M-estimation. The variance of the pose is calculated based on 1-order approximation inference, which enables EKF to generate the final pose estimation and predict the pose at the next time instant. Sufficient experiments validate the accuracy and the efficiency of the proposed method.
The Impact of Experimentally Induced Limb Ischemia on the Rubber Hand Illusion

The rubber hand illusion (RHI) is a perceptual illusion in which one experiences an object as part of their body when synchronous visuo-tactile stimulation is applied to one’s hand and the object. There are a variety of factors that can impact the occurrence of the RHI. In the present study, we demonstrate that experimentally induced peripheral ischemia can impact the RHI, namely it can result in larger alterations to the perceived position of one’s hand. This study suggests that alterations to the cardiovascular system may be a source of individual differences in the RHI literature. Future studies with samples of individuals with cardiovascular pathology are recommended.
On the Helpfulness of Document Context to Sentence Simplification

Most of the research on text simplification is limited to sentence level nowadays.In this paper, we are the first to investigate the helpfulness of document context on sentence simplification and apply it to the sequence-to-sequence model.We firstly construct a sentence simplification dataset in which the contexts for the original sentence are provided by Wikipedia corpus.The new dataset contains approximately 116K sentence pairs with context.We then propose a new model that makes full use of the context information.Our model uses neural networks to learn the different effects of the preceding sentences and the following sentences on the current sentence and applies them to the improved transformer model.Evaluated on the newly constructed dataset, our model achieves 36.52 on SARI value, which outperforms the best performing model in the baselines by 2.46 (7.22%), indicating that context indeed helps improve sentence simplification.In the ablation experiment, we show that using either the preceding sentences or the following sentences as context can significantly improve simplification.
The Slow Deterioration of the Generalization Error of the Random Feature Model

The random feature model exhibits a kind of resonance behavior when the number of parameters is close to the training sample size. This behavior is characterized by the appearance of large generalization gap, and is due to the occurrence of very small eigenvalues for the associated Gram matrix. In this paper, we examine the dynamic behavior of the gradient descent algorithm in this regime. We show, both theoretically and experimentally, that there is a dynamic self-correction mechanism at work: The larger the eventual generalization gap, the slower it develops, both because of the small eigenvalues. This gives us ample time to stop the training process and obtain solutions with good generalization property.
Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

This paper introduces an adaptable, human-computer interaction method to control multirotor aerial vehicles in unsupervised, multi-subject environments.A region-based convolutional neural network (R-CNN) first detects subjects in a frame and their faces' regions of interest (RoIs), which are then fed to a facial recognition module to search for the main subject within the frame.The R-CNN model supplies the right-hand RoI of the main subject to a convolutional neural network (CNN) that classifies the right-hand gesture.A motion processing unit (MPU) and four flex sensors are embedded in the left hand's smart glove to produce discrete and continuous signals.Those signals are generated based on the bending of left-hand fingers and the left hand's roll angle and then fed to a support vector machine (SVM) to classify the left-hand gesture.Three validation layers have been implemented, including a human-based validation, classification validation, and the system validation.The comprehensive experimental results have validated the proposed method.
Neural Grammatical Error Correction for Romanian

Resources for Grammatical Error Correction (GEC) in non-English languages are scarce, while available spellcheckers in these languages are mostly limited to simple corrections and rules. In this paper we introduce a first GEC corpus for Romanian consisting of 10k pairs of sentences. In addition, the German version of ERRANT (ERRor ANnotation Toolkit) scorer was adapted for Romanian to analyze this corpus and extract edits needed for evaluation. Multiple neural models were experimented, together with pretraining strategies, which proved effective for GEC in low-resource settings. Our baseline consists of a small Transformer model trained only on the GEC dataset ( F0.5=44.38), whereas the best performing model is produced by pretraining a larger Transformer model on artificially generated data, followed by finetuning on the actual corpus ( F0.5=53.76). The proposed method for generating additional training examples is easily extensible and can be applied to any language, as it requires only a POS tagger.
Authentication framework for security application developed using a pictorial P300 speller

ABSTRACT With the recent advancement in the field of information technology, online frauds have also been increased and hence password-based systems are no more secure. Therefore need for a more reliable authentication system has been growing rapidly. This study focuses on a P300 based authentication system designing. The proposed study presents a new four-choice oddball paradigm where a conventional 6 × 6 character-based speller is replaced by a novel 2 × 2 pictorial speller. In this study, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Quadratic Support Vector Machine (QSVM). Out of these three, QDA performed better. The results showed that the designed visual stimuli obtained significantly higher accuracy (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The results also show that reduced matrix size and modified stimulus typically affects the accuracy and communication speed of a P300 based authentication system.
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context

This paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context.Based on this evaluation, we selected seven feature sets from the literature, amounting to 65 distinct linguistic features.The features were then grouped into 9 broad classes.After building Random Forest models, we used Feature Importance Ranking and Sequential Forward Search methods to assess the "importance" of the features.Combining the results of the two methods, we propose a consensus feature set.The 6 features in our consensus set come from 4 different classes, namely grammatical role, inherent features of the referent, antecedent form and recency.
Effect of Trade Openness and Financial Openness on Economic Growth in Sub-Saharan African Countries.

The study examines the individual and joint effects of trade openness and financial openness on economic growth in sub-Saharan African (SSA) countries within the period 1980 and 2017. The SSA countries are divided into two broad categories-low income countries and middle-income countries. The dynamic panel analysis using the techniques of Difference Generalised Method of Moments (GMM) and system GMM were employed. Overall, the empirical findings on low income countries show that trade openness has significant positive impact on economic growth. However, financial openness and the joint trade and financial openness do not have significant positive impact on economic growth. In the case of middle-income countries, the effect of trade openness on economic growth is mixed. However, both financial openness and the joint trade and financial openness do not spur economic growth. Overall, there is no evidence of simultaneous openness hypothesis in SSA economies. Thus, while the economy is open to trade, it is expedient to ensure that appropriate and productive Greenfield   foreign direct investments are attracted to SSA economy.
Medical Images Enhanced by Using Fuzzy Logic Depending on Contrast Stretch Membership Function

Medical images are often adversely affected by a lack of clarity due to the limited representation of the color gamut. In this research, three types of medical images microscopic, magnetic resonance and x-ray images were enhanced by using a Fuzzy Logic by Stretch Membership Function (FLSMF). The Stretch Membership Function increased the dynamic range for the compounds red, green and blue in the medical images which have a few ranges. The FLSMF algorithm was compared with other methods by calculating the entropy value, wavelet quality evaluator and lightness order error. The analysis of the results showed that the proposed method succeeded in enhancing the contrast of the different types of medical images where it had high average values for the entropy (6.95) and wavelet quality evaluator (0.08), and a small average value for the lightness order error (60.77).
Binary classification of floor vibrations for human activity detection based on dynamic mode decomposition

Analyzing small amplitude of floor vibrations is a new promising means for identifying the types of human activities, e.g., walking around and accidental falls. In this paper, we consider the binary classification problem of floor vibrations for the applications like fall detection. For practical use, there are two main issues of the problem. First, the prediction of the classifier should be fast. Second, the training set is sometimes small and the diversity of negative samples brings extra challenges when the training samples are insufficient. The state-of-the-art methods for time series classification, such as HIVE-COTE and ResNet, are computationally intensive or susceptible to the size of the training set. Therefore, we propose a new feature extraction method based on dynamic mode decomposition (DMD) and high-frequency characteristics, whose time complexity is linear with the size of training set and quadratic with the length of time series. The method is evaluated on the dataset of floor vibrations proposed by Madarshahian et al. (2016). The results show higher accuracy compared to the ResNet classifier and time series forests, especially when the negative training samples are deficient in type.
An Exploratory Study on Multilingual Quality Estimation

Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages. An obvious disadvantage of this approach is the need for labelled data for each given language pair. We challenge this assumption by exploring different approaches to multilingual Quality Estimation (QE), including using scores from translation models. We show that these outperform single-language models, particularly in less balanced quality label distributions and low-resource settings. In the extreme case of zero-shot QE, we show that it is possible to accurately predict quality for any given new language from models trained on other languages. Our findings indicate that state-of-the-art neural QE models based on powerful pre-trained representations generalise well across languages, making them more applicable in real-world settings.
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification

We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments. The attention-worthy words in arguments are crucial clues for classifying the discourse relations. Attention mechanisms have been proven effective in highlighting the attention-worthy words during encoding. However, our survey shows that some inessential words are unintentionally misjudged as the attention-worthy words and, therefore, assigned heavier attention weights than should be. We propose a penalty-based loss re-estimation method to regulate the attention learning process, integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. We conduct experiments on the Penn Discourse TreeBank (PDTB) corpus. The test results show that our loss re-estimation method leads to substantial improvements for a variety of attention mechanisms, and it obtains highly competitive performance compared to the state-of-the-art methods.