Reviewerly/exHarmony huggingface dataset:
Reliable automatic word spacing using a space insertion and correction model based on neural networks in Korean

Automatic word spacing in Korean remains a significant task in natural language processing owing to the extremely complex word spacing rules involved. Most previous models remove all spaces in input sentences and insert new spaces in the modified input sentences. If input sentences include only a small number of spacing errors, the previous models often return sentences with even more spacing errors than the input sentences because they remove the correct spaces that were typed intentionally by the users. To reduce this problem, we propose an automatic word spacing model based on a neural network that effectively uses word spacing information from input sentences. The proposed model comprises a space insertion layer and a spacing-error correction layer. Using an approach similar to previous models, the space insertion layer inserts word spaces into input sentences from which all spaces have been removed. The spacing error correction layer post-corrects the spacing errors of the space insertion model using word spacing typed by users. Because the two layers are tightly connected in the proposed model, the backpropagation flows are not blocked. As a result, the space insertion and error correction are performed simultaneously. In experiments, the proposed model outperformed all compared models on all measures on the same test data. In addition, it exhibited reliable performance (word-unit F1-measures of 94.17%∼97.87%) regardless of how many word spacing errors were present in the input sentences.
Study of Lighting and Material Iterations in Full Scale Model Using Virtual Reality and Interactive Architectural Representation

Common architectural visualization is using 3D digital model represented in 2D media such as screen and papers. However, this method has two limitations. First, it does not have a full scale to provide a true perception space. Second, the 2D media to represent the 3D model is static. These limitations diminish the user's experience and understanding of space. Currently, architectural visualization is starting to utilize technological advances of virtual reality (VR) and interactive game engine. The full-scale projection in VR can minimize the perception gap between the client's and the designer's idea. It also has interactivity that enables users to explore the space and perform direct manipulation at some design aspects. This paper observes how VR and interactive technology is used to explore scale, lighting experience, and material iterations. The research was conducted for one semester in 2 courses. In these courses, students worked in groups to create, experience, test, and evaluate their design through 3D VR environment and interactive representation. Afterwards, the VR was tested to common users. We examined the enhancement that users get by experiencing design through VR compared to viewing the architectural visualization on picture and discuss how this could be improved and integrated to curriculum.
Item Selection Methods for Computer Adaptive Testing With Passages

Computer adaptive testing (CAT) has been shown to shorten the test length and increase the precision of latent trait estimates. Oftentimes, test takers are asked to respond to several items that are related to the same passage. The purpose of this study is to explore three CAT item selection techniques for items of the same passages and to provide recommendations and guidance for item selection methods that yield better latent trait estimates. Using simulation, the study compared three models in CAT item selection with passages: (a) the testlet-effect model (T); (b) the passage model (P); and (c) the unidimensional IRT model (U). For the T model, the bifactor model with testlet-effect or constrained multidimensional IRT model was applied. For each of the three models, three procedures were applied: (a) no item exposure control; (b) item exposure control of rate 0.2 ; and (c) item exposure control of rate 1. It was found that the testlet-effect model performed better than passage or unidimensional models. The P and U models tended to overestimate the precision of the theta or latent trait estimates.
Enhancing Deep Learning with Visual Interactions

Deep learning has emerged as a powerful tool for feature-driven labeling of datasets. However, for it to be effective, it requires a large and finely labeled training dataset. Precisely labeling a large training dataset is expensive, time-consuming, and error prone. In this article, we present a visually driven deep-learning approach that starts with a coarsely labeled training dataset and iteratively refines the labeling through intuitive interactions that leverage the latent structures of the dataset. Our approach can be used to (a) alleviate the burden of intensive manual labeling that captures the fine nuances in a high-dimensional dataset by simple visual interactions, (b) replace a complicated (and therefore difficult to design) labeling algorithm by a simpler (but coarse) labeling algorithm supplemented by user interaction to refine the labeling, or (c) use low-dimensional features (such as the RGB colors) for coarse labeling and turn to higher-dimensional latent structures that are progressively revealed by deep learning, for fine labeling. We validate our approach through use cases on three high-dimensional datasets and a user study.
Depth perception of moving objects viaing structured light sensor with unstructured grid

Structured light (SL) has been extensively researched and developed due to reconstruction the fast and highly accurate depth sensing. In order to achieve this goal, many approaches have been taken. In this article, we have taken some innovative work in the field for building a new type of fast, high-precision depth perception based on structured light. In addition, the basic ideas we propose are as follows: our model for slow motion dynamic scenes, the sparse depth maps are obtained based on the pair of unstructured rigid pattern with different color, and the high-quality depth images are optimized by the ridge line extraction. Finally, the calculated two depth maps based on the different color pattern merged, which to acquire the optimization depth. We further propose a model for the high-speed motion dynamic scene which apply a pair of projectors structure light. We show that, we actively use motion blur to estimate depth which is different from the previous approaches. The patterns are illuminated from two projectors and the motion blur of each line is accurately measured. The scene depth information is estimated by analyzing the length of the blur. As a result, we describe two experiments that we conducted on two different experimental equipment (ball and planner board). The first experiment was conducted with the planer board, measuring the slow moving object. In addition, the second experiment scene was conducted with the ball, which obtained the depth of the ultra-fast object. Our experimental results demonstrate that our approach achieves effective real-time measurement, can successfully obtain accurate depth information of moving objects.
A fixed-point algorithm for second-order total variation models in image denoising

In this paper, we construct fixed-point algorithms for the second-order total variation models through discretization models and the subdifferential and proximity operators. Particularly, we focus on the convergence conditions of our algorithms by analyzing the eigenvalues of the difference matrix. The algorithms are tested on various images to verify our proposed convergence conditions. The experiments compared with the split Bregman algorithms demonstrate that fixed-point algorithms could solve the second-order functional minimization problem stably and effectively.
Efficient Human Pose Estimation in Hierarchical Context

Most existing human pose estimation methods focus on enhancing the accuracy performance alone while ignoring the critical model efficiency issue. This dramatically limits their scalability and deployability in large-scale applications. In this paper, we consider the under-studied model efficiency problem in pose estimation. We demonstrate the advantages and potential of hierarchical context learning in the convolutional neural network. Specifically, we formulate a novel hierarchical context network (HCN) architecture that can be trained and deployed efficiently while achieving competitive model generalization capability. This is achieved by progressively forming and imposing multi-granularity context information during the pose regression learning process in a coarse-to-fine manner. The extensive comparative evaluations validate the superiority of the proposed HCN over a wide variety of the state-of-the-art human pose estimation models on two challenging benchmarks: MPII and LSP.
Resolving polite conflicts in predicate agreement

When an agreement controller contains conflicting semantic and formal features, the predicates that agree with it can differ with respect to the type of features they reflect. With the second person pronoun used for politely addressing a single referent, finite verbs always agree in (formal) plural number, while participles in some languages may or must agree in (natural) singular number and gender. Building on the claims from the previous literature that the honorific pronoun formally encodes both the grammatical features (plural number and person) and the natural gender and singular number, the analysis proposed in this paper derives the variation in predicate agreement by combining the formal tools of feature geometries, separate probing and variation in the ordering of Agree operations. The main claim advanced by the paper is that predicates differ with respect to the φ-features they probe for, and the order in which this probing applies.
An Ensemble of Optimal Trees for Software Development Effort Estimation

Accurate estimation of software development effort plays a pivotal role in managing and controlling the software development projects more efficiently and effectively. Several software development effort estimation (SDEE) models have been proposed in the literature including machine learning techniques. However, none of these models proved to be powerful in all situation and their performance varies from one dataset to another. To overcome the weaknesses of single estimation techniques, the ensemble methods have been recently employed and evaluated in SDEE. In this paper, we have developed an ensemble of optimal trees for software development effort estimation. We have conducted an empirical study to evaluate and compare the performance of this optimal trees ensemble using five popular datasets and the 30% hold-out validation method. The results show that the proposed ensemble outperforms regression trees and random forest models in terms of MMRE, MdMRE and Pred(0.25) in all datasets used in this paper.
A Framework for Development of Risk-Informed Autonomous Adaptive Cyber Controllers

This article details a framework and methodology to risk-inform the decisions of an unsupervised cyber controller. A risk assessment methodology within this framework uses a combination of fault trees, event trees, and attack graphs to trace and map cyber elements with business processes. The methodology attempts to prevent and mitigate cyberattacks by using adaptive controllers that proactively reconfigure a network based on actionable risk estimates. The estimates are based on vulnerabilities and potential business consequences. A generic enterprise-control system is used to demonstrate the wide applicability of the methodology. In addition, data needs, implementation, and potential pitfalls are discussed.
Structured light 3D measurement of reflective objects using multiple DMD projectors

Many applications of 3D imaging must deal with objects that are highly specularly reflective and have multiple interreflections, resulting in missing and erroneous points. We use a single camera with multiple DMD projectors, resulting in multiple incident lighting angles to deal with both specular and interreflections. At each camera pixel, multiple 3D point estimates are computed from each of the camera-projector pairs. These multiple estimates are fused together with an expectation maximization (EM) clustering algorithm and the maximum likelihood estimate for each point is obtained. A novel noise filtering algorithm is also presented which makes use of the EM algorithm covariance to suppress spurious points caused by reflections. Results show that our multi-projector approach and EM estimator increase 3D measurement accuracy and recoverable points for complex reflective objects, while reducing error.
Illumination-Invariant Video Cut-Out Using Octagon Sensitive Optimization

This paper presents an effective video cut-out approach, which can be utilized to segment the moving object in video shots. We first introduce the Octagon-Sensitive-Filtering (OSF) and its illumination invariant feature (IIF), which is computed on each pixel of the image via adding contributions from neighboring pixels. We integrate our IIF into the variational model and obtain the seeds during preprocessing to help address large displacement and illumination changes. An effective seed update method based on tracking-then-refinement based on IIF is presented to compensate for location ambiguities, and the strategy is effective to deal with illumination variances and objects deformation. Furthermore, we apply the IIF-based graph-cut to deal with fuzzy boundaries. Multiple experiments on quantitative challenging datasets have shown the robustness, high-quality video cut-out and efficiency of our approach to acute variances of illumination and complex motion.
Modeling a Bulletin Board Service Based on Broadcast Channels with Memory

The publication of the election data is fundamental for making electronic voting systems universally verifiable. For this, voting protocols usually rely on a secure bulletin board, which keeps track of the data produced during the protocol execution. This paper presents a general model for implementing such a bulletin board service. The design of the model is based on the concept of an ideal broadcast channel with memory, which transmits messages without loss of information to a present or future receiver. The challenge of implementing a bulletin board service is to approximate the properties of such an ideal channel to the best possible degree. Our model contributes to a better understanding of these properties and may help in designing future bulletin board implementations.
Efficient 3D stereo vision stabilization for multi-camera viewpoints

In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
The Direction of Fisheries Education for the Fourth Industrial Revolution

The fourth industrial revolution is building on the third industrial revolution and a fusion of new technologies such as physics and biotechnology. It is characterized by hyper-connectivity, super intelligence, and predictability. It is very important to develop the human resource related to fisheries underlying our industry in order to provide for the fourth industrial revolution. Human sources fitting into the field of fisheries are creativity-interdisciplinary type. In order to develop fisheries’ human resources of creativity-interdisciplinary type the suggestions for directions of fisheries education paradigm were as follows: Firstly, it was needed to activate the free semester system including fisheries vocational experience education program. Secondly, through expanding Fisheries Meister high school, it was needed to raise fisheries human sources required in workplace of the fourth industrial revolution by customized training program in school. Thirdly, it was required to change fisheries educational institutions which have been still in a state of traditional frame of fisheries science because paradigm shift was needed to interdisciplinary, transcontinental, inter-major or convergence education(or Yungbokhap education) to provide for a new era of the fourth industrial revolution. Fourthly, it was required to establish entrepreneurship education and vocational retraining education system related to fisheries by lifelong education institution. Lastly, it was needed to study fisheries education related to macroscopic sustainable development goals in global level since fisheries education needs to be done to meet ecosystem-based fisheries management(EBFM) carried forward worldwide.
A New Pixel to Block Mapping for Efficient and High Fidelity Data Hiding

Reversible Data Hiding (RDH) with Pixel-to-Block (PTB) mapping has recently been studied for content authentication and annotation applications of digital images keeping the original pixels intact in an embedded image. This paper presents a new PTB mapping technique and its uses in developing an RDH scheme. Existing PTB techniques generate an image block of size 2×2 from an original pixel using a predefined predictor. For embedding 4-bits of secret data, pixels of each block are then shuffled to form a unique combination of pixels for each value of the 4-bit binary data. In contrast, we propose to compute a 2×2 block copying an original pixel to all four pixels. The first pixel is kept unchanged and the others are modified to construct a unique block mapped to the values of 4-bit data. With the proposed minimum possible changes in the three pixels of a block would offer a better embedded image quality. The effective embedding capacity would also be increased as such the proposed PTB technique can tackle the overflow and underflow problem without any location map. Early experimental results of the proposed RDH scheme with the new PTB mapping recorded a significantly better rate-distortion performance, while compared to the state-of-the-art RDH schemes.
Analytical Comparison of Histogram Distance Measures

This paper presents a comparative study of different distance measures used to compare histograms in applications such as pattern recognition, feature selection, image sorting, grouping, identification, indexing, and retrieval. The focus of the study is on how distance measures are affected by variations across images. Different distances between histograms were investigated and tested to compare their performance in retrieving gray scale and color images. A wide range of review papers on calculating distances between histograms was examined. One comparative study was found where histogram bins having zero value were discarded in the calculus of certain distances. We show that this is an inappropriate approach; our tests revealed that zero-value bins should be included to avoid erroneous calculations and achieve a performance advantage over other distance measures.
Extraction of Semantic Relations in Noisy User-Generated Law Enforcement Data

Relation extraction from text is a well-known and extensively studied topic in Natural Language Processing research. However, the implementation of relation extraction approaches in real-world application scenarios raises various methodological considerations which are often left implicit in existing research. This paper explores these considerations using a real-world dataset of user-generated police reports in Dutch. The use of linguistic features based on dependency trees is investigated, including an ablation analysis of the importance of individual features. The construction of negative examples for machine learning models is discussed, as well as the construction of a baseline model. The methodological implications of using a small dataset are discussed in terms of the design and performance of a Long Short Term Memory network as well as a Support Vector Machine. In general the models perform well, however the definition of the classification task, and in particular the construction of negative examples, are shown to have a large impact on classification accuracy and subsequently on the interpretation of the evaluation results.
Introducing Confidence as a Weight in Random Forest

Random forest is one of the most popular supervised learning methods which is a collection of multiple decision trees. It is computationally fast, easy to use and gives reasonable performances over diversified applications. In a random forest, outcomes from multiple trees are aggregated to make the final decision where all trees get the same importance. However, it is natural that the performance of all trees is not equal and thus calculating the majority vote from these trees may not always give the correct decision. To improve this situation, we propose Confidence weighted Random Forest (CwRF) which uses a confidence factor for each leaf node in every decision tree and use that confidence to take weighted majority vote for making the final decision. A rigorous experiment over twenty-five datasets illustrates that the proposed CwRF outperforms three other state-of-the-art methods and provides significantly better performance over nineteen datasets out of twenty-five datasets at best.
A Bayesian random partition model for sequential refinement and coagulation

We analyze time-course protein activation data to track the changes in protein expression over time after exposure to drugs such as protein inhibitors. Protein expression is expected to change over time in response to the intervention in different ways due to biological pathways. We therefore allow for clusters of proteins with different treatment effects, and allow these clusters to change over time. As the effect of the drug wears off, protein expression may revert back to the level before treatment. In addition, different drugs, doses, and cell lines may have different effects in altering the protein expression. To model and understand this process we develop random partitions that define a refinement and coagulation of protein clusters over time. We demonstrate the approach using a time-course reverse phase protein array (RPPA) dataset consisting of protein expression measurements under different drugs, dose levels, and cell lines. The proposed model can be applied in general to time-course data where clustering of the experimental units is expected to change over time in a sequence of refinement and coagulation.
Teachers’ Professional Development for International Engineering Education in English

The global world determines internationalization as one of the trends of higher education development, including higher engineering education. In the sphere of international education there is a strong competition among universities. One of the approaches to attracting more students from other countries is offering study programs in English to help them to overcome the language barrier. Before introducing programs in English for international students, Moscow Automobile and Road Construction State Technical University (MADI) decided to design a special program aimed at training educators for teaching technical disciplines in English. The goal of the project is to design a new teachers’ professional development program intended to improve their communicative competence in English. The paper describes the technology of the curriculum design, the educational objectives and the structure of the program, universal, general professional and professional competencies to be developed, the disciplines included in the curriculum.