Reviewerly/exHarmony huggingface dataset:
초등과학 수업에서 오조봇 코딩을 활용한 SW 융합교육프로그램의 개발과 적용

The purpose of this study is to investigate the effects of the SW-STEAM program by utilizing ozobot coding for elementary science class. The developed SW-STEAM program was applied to 5th grade students of S elementary school located in B city. The participants were divided into an experimental group which is consisted of 22 students and a comparative group comprised 22 students. And they were engaged in different teaching and learning methods during 11 class hours. The experimental group participated in the SW-STEAM program, the comparative group was taught by using a traditional instruction. The main results of this study are like followings. The SW-STEAM program had a positive effect on elementary school students’ basic science process skill, science learning motive, science achievement. Therefore, the SW-STEAM program by utilizing ozobot coding could be meaningful works to encourage students’ basic science process skill, science learning motive and science achievement, and more studies on developing SW-STEAM program are needed.
Trajectory Data Publication Through Individualized Sensitive Stay Location Anonymization

The abundance of GPS embedded devices accumulates trajectories in an excessive scale and it is enriched with personal information. While publishing traces for research activities, we must ensure to publish the anonymized trajectory in order to prevent the disclosure of individual privacy. During anonymization, we need to anonymize the stay locations where the user considered it as most sensitive instead of anonymizing all locations. For finding and extracting the most sensitive stay locations, we adopt a new method by considering the individual spatial and temporal factors using SSLF function. This combines the stay points within a threshold distance to form stay locations and also anonymize these locations in a stay zone using the generalization SSLA approach. The proposed model is tested with a real-world dataset and it guarantees a better trade-off between privacy and utility compared with other models of same nature.
Is There an Interplay Between Library Usage and Repository Features?

The advent of open source has changed the way developers reuse software. The availability of libraries and their corresponding source code in public software repositories enables new forms of analyzing project aspects that can provide clues on their stability and maintainability. However, the literature lacks studies aiming to identify and understand whether and which repository features may correlate with the likeliness of usage of a library. In this sense, we present a factorial experiment using three different regression models - Multiple Linear Regression, Random Forest, and Neural Networks -, aiming at analyzing whether there is a correlation between library usage and a set of features extracted from release management and version control repositories. The results allowed to map features with positive learning impact, such as the number of stars, pull requests, and number of downloads, as well as features that contributed much less to the models (e.g., the repository size). Although the impact level of each feature varied from model to model, we also noticed from the analysis of regression results that the models were capable of achieving higher accuracy when considering only a subset of features.
Peramalan Jumlah Penderita Campak Klinis Di Kota Surabaya Menggunakan Metode ARIMA

Objective : Forecasting is the process of making predictions of the future based on past, needed to determine when the occasion will occur,then the right action can be taken. ARIMA mehthod is a very strong aprroach model for time series analysis. ARIMA models can provide precise study. ARIMA models are very effective to applied in some cases where data show evidence of non-stationarity, shown by the ACF plot which drops exponentially. The ARIMA method is chosen because this model is suitable for all data types including stationary and non-stationary data. If the data is not stationer, where an initial differencing step can be applied to eliminate the non-stationarity. Measles is a highly contagious disease caused by a virus, 90% of children who are not immune can be affected by measles. Forecasting the number of measles sufferers in the city of Surabaya in 2018-2019 is very necessary to see the increasing trend of measles sufferers in the city of Surabaya. Methods : This type of research is non-reactive research. The population in this study is the total number of Clinical Measles sufferers recorded in the Surabaya City Health Office every month in the period between 2012 and 2017. The design of this study is a time series research design, the dependent variable is the number of measles sufferers, whereas the independent variable is time. Results : This study obtained ARIMA model forecasting for the next 2 years amounting to 407 cases in 2018 and 584 cases in 2019. Conclusion : The results of forecasting measles desease that happened in Surabaya have decreased in 2018, from 428 cases that occurred in 2017 to 407 cases, and can be estimated in 2019 to increase to 584 cases. The results of the forecasting can be used as a basis for running more specific programs based on problems.
Towards a Semantic Intelligence to Support Seaport Governance in Environmental and Ecological Sustainability

Taking into account that seaport governance in environmental and ecological sustainability becomes a seaport function of growing public interest, environmental and ecological factors are criteria that will enter into operational choices, capital investments, and cargo routing decisions. Environmental influences as well as factors of institutional governance (regulations and environmental management practices) may lead to common interests among the members. We propose the use of a semantic-based approach to further look at how the knowledge needed to understand the effects observed in seaport decision-making tackle the difficulty of sharing information and finding the best possible representations (model) in order to aid collective decision-making process. The benefits of the semantic use of terminology relevant for the port governance are envisaged in which modeling an ontology will require consensus among domain experts from many and different port areas and the complexity of reaching consensus when multiple conflicting views may exist can threaten consistency across the systems.
An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics

In this chapter, authors explore keystroke dynamics as behavioral biometrics and effectiveness of state-of-the-art machine learning algorithms for identifying and authenticating users based on keystroke data. One of the motivations is to explore the use of classifiers to the field keystroke dynamics. In different settings, recent machine learning models have been effective with limited data and computationally relatively inexpensive. Therefore, authors conducted experiments with two different keystroke dynamics datasets with limited data. They demonstrated the effectiveness of models on dataset obtained from touch screen devices (mobile phones) and also on normal keyboard. Although there are similar recent studies which explore different classification algorithms, their main aim has been anomaly detection. But authors experimented with classification methods for user identification and authentication using two different keystroke datasets from touchscreens and keyboards.
Using Growing Neural Gas Networks for Clustering of Web Data as a Foundation for Marketing Automation in Brick-and-Mortar Retailing

Even though more than 90 percent of retail executives agree to personalization in marketing being a top priority for them, only a handful deliver on this aspect. In brick-and-mortar retailing marketing actions that are conducted for large-scale grid based on an arbitrarily set distribution area - not tailored to single stores. This is mainly due to the lack of data and missing processes for automatization of marketing management. Even though the collection of data is easy to establish, the resulting data quality and condition is a problem for many clustering algorithms. Even methods of Machine Learning that are considered to be more robust to noise have issues with the unpredictable nature of such data collection, as they rely on a predefined structure. This paper presents a method for collecting a large amount of relevant location information for a major German brick-and-mortar retailer from online data sources and mapping them to a grid model of Germany at the level of 1 km2. The clustering is conducted using methods of Machine Learning, in particular Growing Neural Gas (GNG), a neural network variation. The resulting store clusters are then tested for the purpose of location specific marketing. The GNG adapts good to the noisy data and the practical quality is feasible.
Why the Build-Versus-Buy Decision is Difficult in Developing Embedded Systems

The build-versus-buy decision is very difficult when developing embedded systems. Most of the time, the problems are not technical as they are the results of human nature and its failings. Commitment to one another and to the project, along with a negotiated plan can failure to meet schedule, budget, and requirements.
I will never forget you: Direct forgetting and the 3-state model of visual working memory.

The 3-state model of visual working memory (VWM) proposes three states of memory: Focus of attention (FA), direct access region (DAR), and activated LTM (aLTM), with the FA and DAR representing VWM and aLTM representing a relatively active portion of LTM. Previous research has found the item in the FA to be the most accessible, followed by the item in the DAR, and then task relevant items in aLTM are passively maintained. The goals of the present study were (1) to look for evidence for the 3-state model, and (2) to examine how information is forgotten from VWM and aLTM by using a directed forgetting paradigm. In previous research, the directed forgetting paradigm resulted in a directed forgetting benefit – memory for remember items is significantly better when item(s) are directed to be forgotten. Within the 3-state model, it could be that information is dropped completely from all three states resulting in a directed forgetting benefit across all states. Using eye tracking and gaze-contingent trials, participants were presented six faces on each trial in sequential order. On cue-to-forget trials, after viewing all faces in a trial, an arrow pointed at one face for 500 ms and following a visual mask, participants were shown one face and asked, “Did this face change?” All cue trials were valid and participants were never tested on a cued-to-forget face. The present experiment found support for the 3-state model of VWM, but did not find a directed forgetting benefit for any of the states. This experiment suggests that a directed forgetting benefit is not found with complex stimuli and that the 3-state model of VWM can be robust against directed forgetting.
Large enhancement of simultaneous color contrast by surrounding white gap, but not by black gap

We found an illusion of which thin (0.031 deg) gray lines on a cyan background appeared red when the lines were surrounded by thin (0.031 deg) white gaps. This effect was known as simultaneous color contrast, however, the magnitude of the effect was greatly enhanced than the line without gaps. The effects were quantified by a color-matching experiment, and matched colors were significantly shifted toward a complementary hue than no gap conditions (p < 0.05, permutation test). Generally the appearance of gray lines on a color background reflects either simultaneous color assimilation or contrast depending on its luminance, whereas gray lines with white gaps always showed simultaneous color contrast regardless of gray luminance levels. Moreover, almost no simultaneous contrast nor assimilation was observed if the gap was black. This indicates any explanation based on spatial frequency or proximity would be difficult. The illusion occurred regardless of hues if the appropriate thickness of gray lines and gaps were used; preferred thickness was larger for blue and yellow (S cone axis) background than cyan and pink background (L-M cone axis). The illusion was prominent for thin lines and dots, however optical blur and chromatic aberration were not major factors. It might reflect the interaction between color and luminance in early vision stages which have small receptive fields. This phenomenon may be considered as an inference of color of small or thin objects which exceeds spatial resolution of color by remaining luminance pattern, and it help to recognize thin objects which have the specular reflection on the side edge, such as thin branches of a tree on the sky background.
Consistent-linear FS-domains

In this paper, we consider bounded-complete domains with maps which preserve existing suprema, and introduce consistent-linear FS-domains. The main conclusions include: 1. The category with consistent-linear FS-domains as objects and Scott continuous functions as morphisms is a cartesian closed category. 2. A bounded-complete domain is completely distributive iff it is a distributive consistent-linear FS-domain. 3. A pointed dcpo L is continuous iff the consistent Hoare powerdomain HC(L) over L is continuous iff HC(L) is a distributive consistent-linear FS-domain.
Differences in 2D and 3D Simulation’s Impact on the Simulation Sickness

In recent years, a shortage of professional drivers has been observed in many countries. It is estimated that currently in Poland 100,000 only active drivers are missing [14]. It results from e.g. too high costs of training, low availability of training centers, rising employee requirements and an negative image of the driver’s profession. At the same time, there are many defavorised groups in the labor market (NEETs, people over 50, immigrants). These people could fill the shortage of professional drivers on the market as soon as they complete their training. In order to decrease the training costs, an alternative to the conventionally conducted training is the use of virtual reality. Unfortunately, the occurrence of simulator sickness may become an important problem hindering its wide implementation. This can also be an important factor affecting the possibility of driver training and its effectiveness. The tests were carried out as a part of ICT-INEX project. The simulations were carried out using HTC VIVE PRO and Oculus Rift VR headsets as well as a computer screen, aiming to determine the possibility of using these tools in professional driver training. The research group included a group of 15 men aged 20–29 (NEETs) and 15 men over 50 years old. In order to determine how the particular simulations influenced the occurrence of simulation sickness symptoms, an analysis was carried out using the RSSQ questionnaires [8, 9]. The impact analysis of particular display types on the occurrence of simulation sickness was carried out.
Research and Implementation of Anti-occlusion Algorithm for Vehicle Detection in Video Data

Object detection is an important branch of image processing and computer vision, which has become a hot research issue in recent years. Accurate target detection in a video is the foundation of intelligent surveillance system. Since the background scenario is dynamic and even especially complicated when vehicles occlude, the target detection accuracy is declined. Therefore, based on the bounding box regression algorithm, this paper constructs adjacent punishment mechanism to make the bounding box clear off other objects. The proposal weak confidence suppression is leveraged for the robustness of the detector when occlusion happens. Experiments show that the proposed method outperforms traditional methods on three different datasets.
Tamura Coarseness for Evaluating OTH Radar Image and Its Application in RFI Suppression

This paper introduces Tamura coarseness as a reasonable index for evaluating the quality of range-Doppler (RD) map in over-the-horizon (OTH) radar. Tamura coarseness is calculated as the texture coarseness of a gray image which is transformed from the RD map. Our analysis shows that Tamura coarseness can correctly reflect the radio frequency interference (RFI) in the RD image and is also robust when the gray transforming function changes. As an example of applications, this paper uses Tamura coarseness to improve the interference suppression algorithm, so that the suppression can be optimized adaptively. Simulation results show that Tamura coarseness can be used as a quality index of the RD image and helps to suppress the interference perfectly.
Optimal Control of Sensor Based Human Arm Motion and Gesture Replication System

Optimal control stabilization of the Human arm motion and gesture replication system plays a vital role in achieving the desired characteristics of the system, especially, for minimizing the time delay as well as removing any undesired oscillations. The conventional system showed the instability in the output response after operating for a long time. However, Linear Quadratic Regulator (LQR) and State Feedback control techniques have optimally controlled the system and overcome this instability. The transient analysis for both the methods showed that State Feedback method decreased the instability to a fair degree even though some oscillation exists just before reaching stability. On the other hand, LQR technique completely removed the oscillation from the system's response due to the shifting of poles to the left half plane. Therefore, LQR is the best suited control technique for achieving an optimal controlled system.
Scalable Action Mining for Recommendations to Reduce Hospital Readmission

Hospital re-admission problem is one of the long-time issues of healthcares in USA. Unplanned re-admissions to hospitals not only increase cost for patients, but also for hospitals and taxpayers. Action mining is one of the data mining approaches to recommend actions to undertake for an organization or individual to achieve required condition or status. In this work, we propose a scalable action mining method to recommend hospitals and taxpayers on what actions would potentially reduce patient readmission to hospitals. We use the Healthcare Cost and Utilization Project(HCUP) databases to evaluate our approach. All our proposed scalable approaches are cloud based and use Apache Spark to handle data processing and to make recommendations.
Capturing Research Output in the Field of Anthropology: Metadata Design and Lessons Learned

To advocate open science and knowledge development, the Princess Maha Chakri Sirindhorn Anthropology Centre (SAC) recognizes the significance of collocation of scattered research outputs funded by the SAC for the public use. The SAC Research Database was developed and launched in March 2019 to provide free access to digital full-text research outputs under the creative common license (CC-BY-NC-ND 3.0). The ease of use and interoperability are taken into consideration when selecting the metadata scheme. The Dublin Core Metadata Element Set was chosen with some modified elements for the SAC Research Database. This paper presents the lesson learned from the development of this database.
CTScanTool, a semi-automated organ segmentation tool for radiotherapy treatment planning

Abstract The goal of radiation therapy is to eradicate tumors while sparing normal tissues from radiation damage. Therefore, accurate definition of tumor and organ boundaries is important for achieving good treatment outcome. Segmentation of tumors and organs at risk is a standard procedure done by radiation oncologists for radiation therapy treatment planning, for which computed tomography (CT) images are commonly used. In this paper, we present the development of CTScanTool, the organ segmentation tool with a graphical user interface, allowing semi-automated and manual delineation. The proposed semi-automated segmentation method is based on the combined techniques of region growing, morphological reconstruction, and watershed transform algorithms. The semi-automated segmentation results of CT images were compared with those contoured by experienced radiation oncologists. In the experiment, we investigated the combined segmentation method on brain, lung and kidney tissues. In all cases, the true positive rate (sensitivity) was higher than 95%. Jaccard indices and positive predictive values (precision) were higher than 91% for brain and lung tissues and better than 81% for kidney tissues. In the near future, CTScanTool will be incorporated in the Proton pencil beam Scanning treatment PLANning system (PSPLAN) used for research and education in proton therapy.
Optimization of Wireless Ad Hoc Network Node Layout Self-play Based on AlphaZero Algorithm

The success of AlphaZero algorithm in chess games provides ideas and methods for other fields. This research designs an intelligent deployment model of wireless ad hoc network node based on the deep reinforcement learning framework of AlphaZero algorithm. Aiming at the difficult problems of setting learning rate and preventing model overfitting in the process of model self-training optimization, this paper chooses gradually decreasing dynamics. Learning rate and regularization method are increased to ensure fast convergence in model training and improve the accuracy of model prediction.
When do you find the next item?: Using occluders to uncover the time course of visual foraging

When searching for multiple instances of a target, observers might wait until they collect one target before beginning to look for the next. Alternatively, they could be searching ahead. We developed a novel procedure for tracking attention during visual foraging search. Subjects collected 4, 7, or 10 T shapes among 20, 17, or 14 Ls. Both targets and distractors were intermittently occluded by filled squares, in a regular on-off cycle, each with a randomly selected phase. There were three visibility conditions: 1s-on/3s-off, 4s-on/4s-off, and continuously visible. Subjects were instructed to click on targets as quickly as possible, regardless of whether they were visible or occluded when clicked. Since targets must be visible in order to be located, we can infer when a target was found from its history of visibility. Averaged across visibility conditions, peak visibility for targets was 719 ms before the click on that target location. Importantly, peak visibility for the next target occurred 30 ms before the click on the current target. These results are consistent with eye tracking data in unoccluded search. Here, subjects first fixate a target 610 ms before the corresponding click, and fixate the next target 66 ms after the click on the current item. The peak of the visibility distribution shifts toward later time points when fewer targets remain in the display, and it takes longer to find the next target. In a control experiment, all items were randomly shuffled between locations on each click, forcing observers to re-start the search after each click. Here, the peak of the visibility distribution for the next item was 374 ms after the click on the current target. Together, these results indicate that observers search for and find the next item before collecting the current item.