Reviewerly/exHarmony huggingface dataset:
A Study on the Diagnostics Method for Plant Equipment Failure

Recently, in the era of the Fourth Industrial Revolution, the rapid development of ICT (Information and Communication Technology) and IoT (Internet of Things) technology have been actively applied to collect and utilize the status data of plant equipment during their operation period. With these technologies it is very important to keep the availability and reliability of the equipment during its usage period without any interruption or failure. In this vein, the CBM (Condition Based Maintenance) or PHM (Prognostics and Health Management) policy which carries out maintenance activities based on the condition of the equipment has been increasingly applied to the plant industry. Although it has a high potential to derive the important value from operation data of plant equipment through data analytics, research on data analytics in the plant industry is still known as an early stage. In this study, we briefly introduce a method to diagnose the fault state of the equipment by detecting patterns related to the failure modes of equipment based on gathered sensor data. To develop the method, we apply the well-known clustering/classification algorithms and text mining and information retrieval method. In a case study, we apply the proposed method and show its possibility throughout preliminary experiments.
Digital Signature Based Control Integrity for JPEG HDR Images

“A ship is always safe at the shore, but that is not what it is built for.”-Albert Einstein. The same philosophy is applied to picture files saved safety on storage devices. This is, however, not always the case. Sharing and exchanging image data through computer networks affect their security. The issue that arises is how to protect these visual contents from malicious attacks. To alleviate this problem, an image verification feature can be exploited to automatically verify the integrity of these contents. In this way, users can detect any intentional corruptions, which can occur at any time during transmission. In the present work, we propose a selective authentication system adapted to HDR images. The main contribution consists in verifying the integrity of several parts from JPEG-HDR files by using a content-based watermarking method. Specifically, a couple of local digital thumbprint and digital signature is calculated based on the SHA-2(256 bits) secure hash algorithm. Then, a verification process is performed by using two different secret keys (private and public RSA keys). Our HDR image verification scheme is applied in the DCT transform domain and maintains backwards compatibility with the legacy JPEG standard. A performance analysis and comparison with related work demonstrate the effectiveness of the proposed approach.
The Longitudinal Study using Autoregressive Cross-Lagged Model: Relationship between the Self-understanding as a Learner and the Perception of the Learner-centered Instruction

본 연구는 학생의 학습자로서의 자기이해와 학습자중심수업에 대한 인식 간의 관계를 종단적으로 살펴보고 두 변수의 인과관계와 방향성을 검증하고자 자기회귀교차지연모형을 적용하였다. 경기교육종단연구(GEPS) 중1패널 1～3차년도 데이터를 사용하였고 주요 연구변인에 대한 응답의 연계성을 고려하여 일부 결측을 제외한 남학생 1,945명(51.6%), 여학생 1,821명(48.4%)으로 구성된 총 3,766명 자료를 최종 분석에 활용하였다. 연구결과, 학습자로서 자기이해 변수와 학습자중심수업 인식 변수는 서로 영향을 주고받는 상호인과관계를 가지는 것으로 나타났다. 두 변인 간 교차회귀경로를 살펴본 결과, 이전 시점 자기이해는 이후 시점 학습자중심수업 인식에 정적인 영향을 미치고, 이전 시점 학습자중심수업 인식은 이후 시점 자기이해에 정적인 영향을 미치는 것으로 나타났다. 두 교차회귀경로는 통계적으로 유의하였고 표준화된 구조계수 추정치를 통해 경로 간 상대적 영향력을 비교하면 자기이해가 학습자중심수업 인식에 미치는 영향력이 반대의 구조관계보다 약 1.6배 높게 나타났다. 본 연구는 자기주도학습의 하위요인에 해당하는 학습자로서 자기이해와 학습자중심수업에 대한 인식이 중학교 시기 3년 동안 통계적으로 유의한 종단관계를 보일 수 있음을 확인하고 이를 토대로 학생이 인식하는 학습자로서 자기이해와 수업방식에 대한 함의와 연구의 제한점을 논의하였다.This study aims to analysis of the longitudinal relationship between the student s self-understanding as a learner and the perception of the learner-centered instruction. To verify the causality and direction between these two variables, autoregressive cross-lagged modeling were applied to Gyeonggi Educational Panel Study(GEPS) data. The results were as follows. First, each variables are statistically significant effect in accordance with the time course from middle school 1st grade to 3rd grade. Second, it turns out that the two variables interact with each other. The previous student’s perception of the learner-centered instruction was had a statistically significant positive effect on the later the self-understanding as a learner. Likewise, the previous self-understanding as a learner was had a statistically significant positive effect on the later the perception of the learner-centered instruction. Both cross regression paths were statistically significant, and based on the relative influence between paths was compared through the standardized estimates of the structural coefficients. As a result of comparison, the influence of self-understanding as a learner to the perception about the instructional methods was about 1.6 times higher than that of the opposite path structure. This study found the statistically significant longitudinal relationship between learners’ perception of the self-centered instruction and the learners’ self-understanding which is a sub-factor of self-directed learning during the three years at middle school and discussed the implication and limitation of student perceived instructional methods and self-understanding.
Path Computation Element communication Protocol (PCEP) extensions for Establishing Relationships between sets of LSPs and Virtual Networks

This document describes how to extend Path Computation Element (PCE)Communication Protocol (PCEP) association mechanism introduced by thePCEP Association Group specification, to further associate sets ofLSPs with a higher-level structure such as a virtual network (VN)requested by clients or applications. This extended associationmechanism can be used to facilitate virtual network control using PCEarchitecture.
On the Logics of Knowledge and Belief

A model theory for a logic of both knowledge and belief (epistemic logic) is spelled out and defended. The chapter explores some consequences of the assumptions that motivate the theory, some alternative ways that the theory might be extended, and some ways in which this kind of model theory might throw light on traditional problems in epistemology. The main focus is the relation between knowledge and belief, and on some parallels between issues that emerged from the project of responding to the Gettier problem and issues in the more formal framework about the relations between the accessibility relations for belief and knowledge operators.
Assessing the Multi-level Knowledge Prominence Perceived by the Authors as Revealed on their Writings

Automatic identification of widely-known facts not only plays a crucial role in automatic curation of databases of common-sense knowledge, but also benefits many applications of natural language processing such as searching, question answering, and common-sense reasoning. For better performance of such automatic identification, it is important to understand the linguistic characteristics of the writings about widely-known facts, if there are such characteristics. In order to investigate these linguistic characteristics, we juxtapose the sentences that report an event and the database entries that contain information about that event. We designed a task of identifying the number of databases in which the event of interest appears. From the experiment, we found that the number of databases to which an event is registered is strongly correlated with the writing style in which the event is described in the literature. This means that the style of writing reveals multi-level understanding of the individual authors on the status of knowledge prominence. To the best our knowledge, our work is the first that suggests an extrinsic evidence supporting the existence of such multi-level cognition. (KAIST)
Incorporating Virtual Reality with Experiential Somaesthetics in an Embodied Interaction Course

Engagement with virtual reality (VR) through movement is becoming increasingly important. Therefore, the VR developers should improve their bodily skills and learn how to use the movement as design material. In addition, first person accounts of the development and experience are necessary. Since these qualities are well-addressed in experiential somaesthetics, we explore the education space in VR, with attention to the first-person experiences, movement data and code. We present an approach for teaching and designing VR-based embodied interaction and describe simple projects implemented by the participants. The evaluation of projects indicates that the concepts, practices, and perspectives of embodied interaction were attained in VR. Our reflections contribute to the movement-based interaction education in VR, and its evaluation and validation by the first-person accounts, in addition to the data and program code produced.
CatE: Category-Name GuidedWord Embedding.

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics that do not fit users' particular needs and yield suboptimal performance on downstream tasks. We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora. This new task not only helps a user understand clearly and distinctively the topics he/she is most interested in, but also benefits directly keyword-driven classification tasks. We develop CatE, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discriminative embedding space and discover category representative terms in an iterative manner. We conduct a comprehensive set of experiments to show that CatE mines high-quality set of topics guided by category names only, and benefits a variety of downstream applications including weakly-supervised classification and lexical entailment direction identification.
Learning Semantic Concepts from Weakly Labeled Data

As in many areas with deep learning methods, great success is achieved in object and scene recognition, but for a good result a large number of labeled data is needed. Our aim in this study is to eliminate the need for tagged data collection that requires too much human labor, and to use the abundant images as data set, paired with the object name on the Internet and social media. However, this data is not as clean as manually labeled data, and it does not work well when used directly. In this study, Association with Model Evolution (AME) method is adapted to eliminate noisy data. Data that were automatically collected and cleaned with AME were then used as the experimental set for Convolutional Neural Networks (CNN). It is observed that the performance is increased by 4% with using the AME cleaned data.
Breast Lesion Classification Based on Shape Features

In this study, it is aimed to classify breast lesions as benign/malignant by using breast magnetic resonance images taken from Sakarya University Training and Research Hospital with legal permission. For this purpose, at the first step breast lesion area is determined exactly by using region growing and Markov random field segmentation methods. Then, feature extraction process is performed by calculating fifteen shape features belonging to the lesion area. At the last step, lesion classification is carried out by utilizing k-nearest neighbor and support vector machines techniques. Radiologist's labeling results obtained according to the BIRADS assessment criteria are used to evaluate the classification performance.
Understanding and improving mobile reading via scalable and low cost sensing

In recent years, due to the increasing ubiquity of Internet and mobile devices, mobile reading on smart watches and smartphones is experiencing rapid growth. Despite the great potential, new challenges are brought. Compared to traditional reading, mobile reading faces major challenges such as encountering more frequent distractions and lacking portable and efficient technique to deeply understand and improve it.Fortunately, the development of the hardware and software of mobile devices provide an opportunity to track users’ behavior and physiological signals accurately in a low-cost and portable manner. In this thesis, I explored the usage of low-cost mobile sensors to solve the measurement challenges of reading.I used the low-cost mobile sensing techniques on mobile devices to understand and improve the degree and quality of reading. In this thesis, I first present SmartRSVP, a reading interface on smart watches that leverages eye-gaze contact tracking technique and heart rate sensing technique to facilitate reading under distractions. I then present Lepton, an intelligent reading system on smart phones that tracks eye-gaze periodical patterns and sensing the screen touching behavior to monitor readers’ cognitions and emotions during reading. Lastly, I present StrategicReading, which uses the implicitly captured eye gaze patterns, scrolling motions, and log histories to monitor users’ reading strategies and performance during multiple-sources online reading.
BENTUK PENILAIAN ASPEK PENGETAHUAN DALAM BUKU TEKS SISWA BAHASA INDONESIA SMA KURIKULUM 2013

The purpose of this research is to describe the assessment form of the knowledge aspects contained in the Indonesian High School Curriculum 2013 textbooks. The research data are phrases, words, clauses, sentences, paragraphs, and discourses that contain content about assessment forms of knowledge contained in the textbook. Data is obtained by documentation techniques then validated by expert judgment and FGD. Data is analyzed with interactive analysis techniques. The results of the research included assessment forms of knowledge aspects of class X and XI in Indonesian textbooks inÂ Curriculum 2013. Assessment Forms of knowledge aspects of class X textbooks include entries, descriptions, question, and answer, and assignments while in class XI include multiple choices, incorrect, fill, description, and assignment.
Semantic approach for building generated virtual-parallel corpora from monolingual texts

Abstract Several natural languages have undergone a great deal of processing, but the problem of limited textual linguistic resources remains. The manual creation of parallel corpora by humans is rather expensive and time consuming, while the language data required for statistical machine translation (SMT) do not exist in adequate quantities for their statistical information to be used to initiate the research process. On the other hand, applying known approaches to build parallel resources from multiple sources, such as comparable or quasi-comparable corpora, is very complicated and provides rather noisy output, which later needs to be further processed and requires in-domain adaptation. To optimize the performance of comparable corpora mining algorithms, it is essential to use a quality parallel corpus for training of a good data classifier. In this research, we have developed a methodology for generating an accurate parallel corpus (Czech-English, Polish-English) from monolingual resources by calculating the compatibility between the results of three machine translation systems. We have created translations of large, single-language resources by applying multiple translation systems and strictly measuring translation compatibility using rules based on the Levenshtein distance. The results produced by this approach were very favorable. The generated corpora successfully improved the quality of SMT systems and seem to be useful for many other natural language processing tasks.
Towards Effective Data Augmentations via Unbiased GAN Utilization

The parameters of any machine learning (ML) model are obtained from the dataset on which the model is trained. However, existing research reveals that many datasets appear to have strong build-in biases. These biases are inherently learned by the learning mechanism of the ML model which adversely affects their generalization performance. In this research, we propose a new supervised data augmentation mechanism which we call as Data Augmentation Pursuit (DAP). The DAP generates labelled synthetic data instances for augmenting the raw datasets. To demonstrate the effectiveness of utilizing DAP for reducing model bias, we perform comprehensive experiments on real world image dataset. CNN models trained on augmented dataset obtained using DAP achieves significantly better classification performance and exhibits reduction in the bias learned by their learning mechanism.
Similarity measurement on digital mammogram classification

Breast cancer is one of the dominant cause of death in the world. The possibility of recovery which is treated at an early step is approaching 95%. Mammography is the gold standard for early detection. In examining mammogram the overall parenchyma pattern of the left and right breast placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building Computer Aided Diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Klinik Onkologi Kotabaru Yogyakarta were used. The first step was enhancing the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by Gray Level Co-Occurrence Matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between features on left and right mammogram. This process was intended to appraise symmetry of left and right mammogram as radiologists did in screening mammography. The results of the classification between normal and abnormal images with Error Back Propagation (EBP) algorithm were accuracy 93.3%, sensitivity 100%, and specificity 83.3%.
A Multi-label Classification Approach to Localization of Multiple Node Failures in Local DC Networks

The wide adoption of network based IT services to support operations and services have driven organizations to deploy local data center (DC) infrastructure and networks. Monitoring the proper functioning of such networks is of critical importance, specially in the event of failures. Timely detection and localization of the failed devices shorten the repair times and guarantee normal operation of infrastructure and services. In this work we propose a data-driven multiple failure localization approach based on device features obtained through passive monitoring. Namely, we set the localization problem as one of multi-label classification using high dimensional and high resolution data that is increasingly available with modern devices. Our results show that using simple base classifiers, the proposed methodology can yield high Hamming accuracy and acceptable compromise on false alarms, without relying on active monitoring.
싱가포르의 체육과 교육과정 개정과 체육 교사 교육: 교육부, 국립교육대학, 지역 학교들의 삼자 간 파트너십

Singapore is taking up challenges of creating a 21st-century education system in a unique tripartite partnership between the Ministry of Education(MOE), the National Institute of Education(NIE) and the local schools. This study aims to explore Singapore''s reform of physical education(PE) syllabus and PE teacher training in such a tight and strong partnership. As compared with 2006 PE syllabus, the recently reformed 2014 PE syllabus looks like that ''Fundamental Movements'' has been replaced with ''Motor Skills and Concepts Framework'' and that ''Outdoor Education'' has been added. However, in the backdrop of these changes are the cooperation of MOE PE, Sports and Outdoor Education Branch, Arts Education Branch, PE and Sports Teachers Academy, the National Institute of Education and the local schools. In order to enhance PE teacher training, MOE establishes and implements sound PE policies and initiatives, NIE strives to train excellent PE teachers, and local schools provide a good environment for student-teachers to perform well in the teaching practice. Singapore''s close tripartite partnership allows research to inform and contribute to PE policies and initiatives and makes it possible for PE policies and initiatives to be translated and implemented seamlessly into local schools.
Materials for visual-world experiment in Grüter, Lau & Ling (2019, LCN)

This folder contains the materials (visual scene image files and sound files for auditory stimuli) reported in Gruter et al. (2019, https://doi.org/10.1080/23273798.2019.1648840).
Efficient Pose Estimation using Random Forest and Hash Voting

Pose estimation is one of the key components in robot perception and exhibits a number of unique challenges. First, it is non-trial to directly search for potential poses in given images. Second, it is very challenging to retrieve pose features hidden in images or point clouds in the presence of textureless objects and occlusion. We present a pose estimation pipeline using RGBD images. We first use random forest to perform segmentation and locate the object of interest in a given RGBD image. Then we generate sufficient hypotheses and compute their possibility distribution using hash voting. Our results show high precision and good performance under severe conditions: textureless objects and occlusion.
Fast Blind Deconvolution with Simple Machine Learning

We show that very fast deblurring can be achieved with simple machine learning. The most difficult step in deblurring is the estimation of the blur kernel. We show that we can estimate the blur kernel by recognizing the object.
Dual Attentive Features for Person Re-identification

Person re-identification (re-ID) plays a vital role in intelligent video surveillance and is the task of retrieving particular persons across different cameras. Effective convolutional features is a key component in person re-ID but how to learn powerful features is still a challenging task due to pose variation, occlusion, and similar appearance among different persons. Considering this fact, we propose a novel dual attention module network which consists of spatial attention and channel-wise attention. Dual attention module add weights on image features in spatial and channel. Attentive features generated by the proposed network are able to better enhance the foreground objects while eliminate background clutter. Intensive experimental results have been prove that the proposed dual attention module network outperforms the state-of-the-art on Market-1501 and DukeMTMC-reID datasets.
A Shape-Aware Structure-Preserving Texture Smoothing Algorithm

In this paper, we introduce a shape-aware texture smoothing algorithm based on a flexible line shift scheme to separate prominent structures from textures or noises. Unlike previous texture smoothing algorithms, the line shift scheme can help to form shape-aware local area for each pixel during filtering. We integrate the line shift scheme with prior knowledge of prominent structures learned from a structured forests classifier into a framework of joint bilateral filtering. This new strategy can greatly improve the efficiency to remove textures/noise on pixels that are close to sharp corners or located in long narrow strip areas, while keep the prominent structures untouched. We demonstrate the performance of the proposed algorithm on a wide variety of images corrupted by textures and Gaussian noise.
Seismic Discontinuous Detection Using Dual-Tree Complex Wavelet Transform

Summary Wavelet transform is an effective way to detect seismic discontinuous structures. Dual-tree complex wavelet transform is a computationally efficient approach and gives good directional selectivity in multi-dimensional signals processing. It hasn&apos;t been applied in seismic discontinuous detection. We proposed a workflow based on dual-tree complex wavelet transform and hard thresholding method. The workflow is applied to an octagon model and field data. The results show its good performance on edge detection and small texture extraction.