Reviewerly/exHarmony huggingface dataset:
Analysis of the Effect of Corporate Employment Creation by Government R&amp;D Support

Recently, as job creation has become a social issue, discussions about job creation through government R&D support are also actively being discussed. In this study, we tried to analyze empirically the effects of government R&D support of Korea on corporate employment. In this study, National Science and Technology Information Service (NTIS) data is used in order to obtain information on the government R&D support in Korea. NTIS data provides information on the support of Korean government R&D projects by year. Total 61,546 R&D projects conducted by the Small and Medium Enterprises (SMEs) from 2011 to 2016 were used for analysis. When analyzing the performance after the government support, it is necessary to distinguish whether the performance is due to the superior characteristics of the enterprise itself or by government support. In this study, Propensity Score Matching (PSM) method and multivariate matching method are simultaneously used for analysis. As a result of the analysis, government R&D beneficiaries showed an increase in terms of both the number of employees and total wage, which mean the quantity of employment. However, it was confirmed that the wage per employee of the beneficiary company decreased. The wage per employee means the quality of employment. Through this study, the employment creation effect of government R&D support and the necessity that both quantitative and qualitative effects should be considered in employment creation were demonstrated empirically.
A GIS-Based tool for Optimizing C-ITS Communication Infrastructure

A GIS-based tool has been developed which estimates and visualizes the coverage from communication infrastructures providing C-ITS services to the road network. The purpose of this tool is to help operators and decision makers design and optimize the communication infrastructure required to support requirements for future C-ITS services. The tool has been constructed as a QGIS plugin, estimating received signal levels along road segments at given locations for roadside units and base stations. It can also estimate where roadside units and base stations should be placed in order to provide optimal coverage for the nearby road network. The communication systems included in the tool are cellular systems (2G/3G/4G), Low Power Wide Area Networks (LPWAN, e.g. LoRa) and ITS-G5. Input parameters include the GPS coordinates of roadside units and base stations, transmitted and received antenna heights, operating frequency, and layers in QGIS that provide both terrain and vegetation information as well as road links. Estimates from the plugin have been compared to measurements of received signal levels for ITS-G5 and 4G communication and have been shown to be relatively accurate.
Extraction of experimental data on chemical kinetics from open sources in the Internet

In article process of intellectual search and extraction of experimental data on chemical kinetics from open sources in the Internet is considered.Approach to the organization of the above-stated process which includes the sequence of the following stages is offered: forming of a computer corpus of documents electronic versions of scientific journals, as in open access, and commercial journals, development of a domain ontology for creation of the thesaurus, intellectual search and forming of the electronic corpus of bibliographic links, the intellectual analysis of bibliographic links on the basis of cluster analysis, downloading, conversion to a test format and document classification by a Bayesian neural network, extraction of data and their warehousing.The technologies developed by authors allow to work with English-language and Russian-language texts.The algorithm automatic classifications uses three methods of a clustering.Automatic classification of publications uses specially constructed three-level information basis which breaks sets of articles on the nature of contents.On the basis of the received splitting the case of electronic documents which contains experimental data on reaction activity of organic compounds in chemical reactions is under construction.The retrieved characteristics of chemical reactions remain in the developed database for further use.
New calibration approach for 3D measurement system based on the light-source-stepping method

In the 3D measurement system based on light-source-stepping method, phase shift changes with the depth of the object changes. The phase distribution recovered by N-step phase shifting method is distorted and then the recovered 3D shape is distorted. Fujigaki proposed the whole-space tabulation method to obtain 3D shape without distortion. The moving stage is used to calibrate the system and then the whole-space look-up tables are constructed. It is inconvenient to calibrate the measurement system. We proposed a new method to calibrate the system without moving stage. Firstly, the camera is calibrated by Zhang’s method. Secondly, a reference plane with sparse marks is placed at different depth of measurement volume. The fringes are projected on the plane and the images are captured at the same time. Thirdly, the phase of different depth is worked out by improved Fourier transform profilometry method. The phase of marker is estimated by local curve fitting. The image coordinates of markers are detected. The projection matrix from reference plane to image plane is worked out from the image coordinates and world coordinates of markers and the inner parameters of camera. The 3D coordinates of every pixel of reference plane are worked out from their image coordinates and the projection matrix. Fourthly, a whole-space look-up table is constructed. This method does not need precise moving stage to calibrate the relationship between phase and 3D coordinates. The hardware requirement for system calibration is simplified. Experiments are carried out to verify the proposed method.
Non-terminating processes in the situation calculus

By their very design, many robot control programs are non-terminating. This paper describes a situation calculus approach to expressing and proving properties of non-terminating programs expressed in Golog, a high level logic programming language for modeling and implementing dynamical systems. Because in this approach actions and programs are represented in classical (second-order) logic, it is natural to express and prove properties of Golog programs, including non-terminating ones, in the very same logic. This approach to program proofs has the advantage of logical uniformity and the availability of classical proof theory.
Short-term Load Prediction of Cloud Computing Based on Fuzzy Information Granulation SVM

In order to predict the short-term load variation range and trend of cloud computing, this paper proposed a prediction model based on information granulation support vector machine (IGSVM). Taking the historical load value as a sample to do simulation training, through Gravitational Search Algorithm (GSA) to optimize the parameters of SVM, and make regression prediction to three parameters of triangular fuzzy particles, Low, R and Up, to obtain the variation range and trend of short-term load. The result is consistent with the actual situation, which verifies the validity of the model and provides the basis for actual operation and maintenance.
Mobile Quiz on Android Platform (MQAP)

In this paper the design and implementation of an interactive mobile application is preparedwithAndroid framework. The proposed system (MQAP), apply quiz sessions in the classroom to the various technical topics. It aims to create a quiz and colonize it with questions and answers. When the student takes the exam, the application will generate random questions for him/her; the students will be able to see their results as soon as they finish the test. Also, the students can see their previous quizzes and their marks. Whenever the Wi-Fi connects to the Internet, in the Android mobiles, users will receive immediate feedback after giving their answers.
Enumeration method of analysis of combinatorial schemes

In the subasymptotic range of variation of parameters of combinatorial schemes, we consider a method of analysis based on explicit enumeration of their outputs in the following directions: enumeration of the output enumeration graph, determination of their number, solution of the numeration problems for outputs in the direct and inverse settings, evaluation of their probability distribution, and construction of an algorithm for their modeling.
Formalisation of Probabilistic Testing Semantics in Coq

Van Breugel et al. [Theor. Comput. Sci. 333(1–2):171–197, 2005] have given an elegant testing framework that can characterise probabilistic bisimulation, but its completeness proof is highly involved. Deng and Feng [Inf. Comput. 257:58–64, 2017] have simplified that result for finite-state processes. The crucial part in the latter work is an algorithm that can construct enhanced tests. We formalise the algorithm and prove its correctness by maintaining a number of subtle invariants in Coq. To support the formalisation, we develop a reusable library for manipulating finite sets. This sets an early example of formalising probabilistic concurrency theory or quantitative aspects of concurrency theory at large, which is a rich field to be pursued.
중학생이 쓴 글의 분량 연구 - 쓰기 과제 구성 요소로서 시간을 중심으로 -

This study observed the amount of writing produced by middle-school students in Korea during time-directed writing tasks. Time-directed writing tasks on familiar topics (e.g., movies, sports) were assigned to first- and second-year middle school students, with time limits of 10, 20, and 30 minutes. There were four significant findings. First, the average of number of Korean characters produced was 261.2 in 10 minutes; 325.5 in 20 minutes; and 571.7 in 30 minutes. The number of t-units was 7.6 in 10 minutes; 10.2 in 20 minutes; and 15.6 in 30 minutes. Second, the amount of writing increased according to the time allowed to the writing task. Third, there was no significant difference in the amount written by first- and second-year students, except when the writing task was limited to 10 minutes. Fourth, the amount of writing was higher among the female students than the male students.
Multi-Target Multi-Camera Tracking with Human Body Part Semantic Features

Recently, Multi-Target Multi-Camera Tracking (MTMCT) has gained more and more attention. It is a challenging task with major problems including occlusion, background clutter, poses and camera point of view variations. Compared to single camera tracking, which takes advantage of location information and strict time constraints, good appearance features are more important to MTMCT. This drives us to extract robust and discriminative features for MTMCT. We propose MTMCT\_HS which uses human body part semantic features to overcome the above challenges. We use a two-stream deep neural network to extract the global appearance features and human body part semantic maps separately, and employ aggregation operations to generate final features. We argue that these features are more suitable for affinity measurement, which can be seen as the average of appearance similarity weighted by the corresponding human body part similarity. Next, our tracker adopts a hierarchical correlation clustering algorithm, which combines targets' appearance feature similarity with motion correlation for data association. We validate the effectiveness of our MTMCT\_HS method by demonstrating its superiority over the state-of-the-art method on DukeMTMC benchmark. Experiments show that the extracted features with human body part semantics are more effective for MTMCT compared with the methods solely employing global appearance features.
Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs

Generative Adversarial Networks (GANs) have enabled researchers to achieve groundbreaking results on generating synthetic images. While GANs have been heavily used for generating synthetic image data, there is limited work on using GANs for synthetically resampling the minority class, particularly for text data. In this paper, we utilize Sequential Generative Adversarial Networks (SeqGAN) for creating synthetic user profiles from text data. The text data consists of articles that the users have read that are representative of the minority class. Our goal is to improve the predictive power of supervised learning algorithms for the gender prediction problem, using articles consumed by the user from a large health-based website as our data source. Our study shows that by creating synthetic user profiles for the minority class with SeqGANs and passing in the resampled training data to an XGBoost classifier, we achieve a gain of 2% in AUROC, as well as a 3% gain in both F1-Score and AUPR for gender prediction when compared to SMOTE. This is promising for the use of GANs in the application of text resampling.
Information Theoretic Modeling of High Precision Disparity Data for Lossy Compression and Object Segmentation

In this paper, we study the geometry data associated with disparity map or depth map images in order to extract easy to compress polynomial surface models at different bitrates, proposing an efficient mining strategy for geometry information. The segmentation, or partition of the image pixels, is viewed as a model structure selection problem, where the decisions are based on the implementable codelength of the model, akin to minimum description length for lossy representations. The intended usage of the extracted disparity map is to provide to the decoder the geometry information at a very small fraction from what is required for a lossless compressed version, and secondly, to convey to the decoder a segmentation describing the contours of the objects from the scene. We propose first an algorithm for constructing a hierarchical segmentation based on the persistency of the contours of regions in an iterative re-estimation algorithm. Then, we propose a second algorithm for constructing a new sequence of segmentations, by selecting the order in which the persistent contours are included in the model, driven by decisions based on the descriptive codelength. We consider real disparity datasets which have the geometry information at a high precision, in floating point format, but for which encoding of the raw information, in about 32 bits per pixels, is too expensive, and we then demonstrate good approximations preserving the object structure of the scene, achieved for rates below 0.2 bits per pixels.
Visual Tracking Using Online Deep Reinforcement Learning with Heatmap

Visual tracking can be formulated as a Markov decision process over a parameterized family of policies. In this case, the problem of visual tracking is to make decisions about whether and how to adjust the agent. This paper introduce an end-to-end methodology which contains a framework from motion prediction to online update for single object tracking. For the prediction phase, we incorporate heatmap with appearance feature to learn a deep metric, and we introduce Region Proposal Network to regress a reliable location. For the online update phase, we take tracking as agent decision making process via learning a policy to pick a proper action about whether and how to update the state transition using Actor-Critic Network. The proposed tracking framework can be trained as an end-to-end fashion, and we demonstrate that our tracking performance is rather competitive with other state-of-the-art visual tracking algorithms. Other than this, from the experimental results we can see that, our prediction network using heatmap learning can acheive rather robust results under usual circumstance, and using reinforcement learning to make online decisions can be helpful when dealing with more complicated cases.
Online Multiscale-Data Classification Based on Multikernel Adaptive Filtering with Application to Sentiment Analysis

We present an online method for multiscale data classification, using the multikernel adaptive filtering framework. The target application is Twitter sentiment analysis, which is a notoriously challenging task of natural language processing. This is because (i) each tweet is typically short, and (ii) domain-specific expressions tend to be used. The efficacy of the proposed multiscale online method is studied with dataset of Twitter. Simulation results show that the proposed approach achieves a higher F1 score than the other online-classification methods, and also outperforms the nonlinear support vector machine.
A Voice for Education: A Digital Analysis of Sheikha Moza’s Public Statements, 2009–2018

Women in politics have been subject to extensive research, and their speeches examined from different gender and discourse analysis perspectives; however, little has been written on female political participation in the Arab world. This paper examines how Sheikha Moza’s public statements have evolved from 2009 to 2018 in terms of linguistic features and content. The method used is both qualitative and quantitative. Unlike previous digital studies to date, this research employs two different digital tools: DocuScope and AntConc. DocuScope was used to trace statistically significant changes and to identify the variables that have changed over time. To complement these results, AntConc was employed in order to analyze unique word choices. Results suggest that there were significant differences between Sheikha Moza’s public statements over the period in question, specifically between the years 2009 and 2018. These were examined and compared here as being predominantly “ceremonial” vs. “realistic”. Thus, in 2009 she adopted a more ceremonial, positive, academic and institutional-idealist stance on education. In comparison, in 2018, she focuses on major international tribulations; her statements contain more narrative, character, negative and descriptive clusters as per DocuScope’s analysis. In particular, in her statements, she makes striking references to human suffering and comes to an understanding that education depends on cultural, political, and economic tranquility. Her language in 2018 becomes much idealist-nationalist and much more realist about the savagery that conflict and war can exact on educational opportunity.
Multi-scale Spatial-Temporal Attention for Action Recognition

In this paper, we propose a new attention model by integrating multi-scale features to recognize human action. We introduce multi-scale features through different sizes of convolution kernel on both spatial and temporal fields. The spatial attention model considers the relationship between detail and integral of the human action, therefore our model can focus on the significant part of the action on the spatial field. The temporal attention model considers the speed of action, in order that our model can concentrate on the pivotal clips of the action on the temporal field. We verify the validity of multi-scale features in the benchmark action recognition datasets, including UCF-101 ( $$88.8\%$$ ), HMDB-51 ( $$60.0\%$$ ) and Penn ( $$96.3\%$$ ). As a result that the accuracy of our model outperforms the previous methods.
예비 초등교사의 사회과 다문화 수업 인식과 교사교육의 함의

The purpose of this study was to identify the characteristics of preservice elementary school teachers by analyzing their perceptions of social studies and multicultural classes, and to address the implications for elementary school teacher education. CoRes (Content Representations) based questionnaires were modified for the purpose of this study and were administered to 25 preservice elementary school teachers. The data was collected and analyzed freely concerning social studies multicultural instructional plans and teacher responses . The results are as follows: First, prospective elementary school teachers chose the most understanding, respect and inclusion of various cultures for the purpose of multicultural classes in social studies. The reason for this is because the increase of domestic multicultural families and the need for global citizenship in the age of globalization are emphasized in teacher education. Second, the preliminary elementary school teachers were able to confirm that the multicultural family students participated in the class and the level of multicultural acceptance was one that elementary school students could accept. Also, when constructing social studies multicultural classes, students mainly used the problem-solving learning model to find solutions to multicultural problems and conducted evaluations centered on values and attitudes. The implications for elementary school teacher education are as follows. First, it is necessary to provide prospective teachers with various multicultural experiences and opportunities in the teacher training process. Second, opinions and ideas on how to evaluate the value attitude of social studies multicultural classes should be discussed more actively and deeply. Third, multicultural teaching logic and concept selection that accurately reflects reality are required from the multicultural teaching material development and educational level.
Wireless Detector for Illicit CATV Tapping

One of the basic applications of television is to give entertainment via cable. Cable companies provide their customers the channels that are broadcasted locally or internationally. Digital Cable TV is fast growing nowadays, but not all companies are ready to adapt to this technology. Analog transmission of cable is still available, however, it is prone to illicit tapping. This study was conducted to detect illicit connections on CATV (Cable Television) lines particularly on analog transmission of signal. The system was conducted on a laboratory scale setup. There was a miniature type CATV distribution line wherein there was a connection from the device to the line. If the system detects illicit connection, then it notifies the user via mobile application. The functionality test was performed to evaluate and validate if the system conforms to its desired tasks. The functions of the system under the evaluation test were a) detection of illicit connection, b) sending of data via Bluetooth and c) receiving of data of the mobile phone. Analysis of the data revealed that the system operated with its desired functions. The results of this study indicate that the developed system was reliable on its functions particularly on detecting illicit connections.
Cybersickness in Virtual Reality: Examining the Influence of the Virtual Environments on Sex Susceptibility

The auspicious future of VR could be thwarted by cybersickness. A factor known to influence susceptibility is sex, with females often experiencing higher incidences. A mitigation strategy is to identify individuals who are more sensitive to cybersickness, such that interventions can be implemented before the onset of subjective symptoms. Such an approach could use predictive models that compare a user’s online kinematic body sway and physiological characteristics to data from individuals that reported cybersickness. If such predictive models can be developed, then one approach is altering the virtual environment (VE) based on this real-time data.
Multicategory large margin classification with unequal costs

In this paper, we propose a multicategory large margin classification with unequal costs. In addition to extending the standard multicategory SVM to the case of unequal costs, we also develop an unequal costs classification for ψ-loss and propose an efficient algorithm for computation. Besides commonly used L2 penalty, the adaptive LASSO is also examined to remove irrelevant variables. Theoretically, we derive the Bayes rules under the generalized cost and reduce the infinite sum-to-zero constraint to a finite constraint. Numerically, we demonstrate the good performance of our methodology on two simulated examples and one real-life dataset.
TiN days and ICL weeks

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