Reviewerly/exHarmony huggingface dataset:
Synrift sandstones and clayey rocks: bulk chemical composition and location on a number of discriminant paleogeodynamic diagrams

The bulk chemical composition of synrift sandstones and associated clayey rocks has been analized, and the distribution of the fields they form has been studied on discriminant paleogeodynamic SiO2K2O/Na2O [Roser, Korsch, 1986] and DF1DF2 [Verma, Armstrong-Altrin, 2013] diagrams. The studied sandstones in terms of bulk chemical composition mainly correspond to greywacke, lititic, arkose and subarkose psammites; Sublitites and quartz arenites are also found. A significant part in the analyzed data massif consists of psammites, in which log(Na2O/K2O)-1.0; missing on the Pettijohn classification chart. This confirms our conclusion, based on the results of mineralogical and petrographic studies, that the sedimentary infill of rift structures unites immature sandstones, the detrital framework of which was formed due to erosion of local sources, represented by various magmatic and sedimentary formations. Synrift clayey rocks, compared with sandstones, are composed of more mature fine-grained siliciclastics. As follows from the distribution of figurative data points of clayey rocks on the F1F2 diagram [Roser, Korsch, 1988], its sources were mainly sedimentary deposits. The content of most of the main rock-forming oxides in the synrift sandstones is almost the same as in silt-sandstone rocks present in the Upper Precambrian-Phanerozoic sedimentary mega-complex of the East European Plate, but at the same time differs significantly from the Proterozoic and Phanerozoic cratonic sediments, as well as from the average composition upper continental crust. It is shown that the distribution of the fields of syntift sandstones and clayey rocks on the SiO2K2O/Na2O diagram does not have any distinct features, and their figurative data points are localized in the areas of terrigenous rocks of passive and active continental margins. On the DF1DF2 diagram, the fields of the studied psammites and clayey rocks are located in areas of riftogenous and collisional environments. We have proposed a different position of the border between these areas in the diagram, which will require further verification.
A Classification-Based Summarization Model Using Supervised Learning

Classification-based summarization model using ID3 and multivariate (CBS-ID3MV) approach produces summaries from the text documents through the process of classification. Extracting the features that are important from a text is one of the most basic tasks. A feature is a set of attributes which captures important qualities of the text document. The approach is to develop a summarizer that takes into account text features from each sentence, for the purpose of classifying summary sentences. The CBS-ID3MV model is trained with DUC 2002 training documents, and the proposed approach’s performance is measured at several compression rates on the data corpus. The results got by the proposed framework works better when compared with other summarizers after evaluation using ROUGE metrics.
Clustering Semantically Related Questions

There has been a vast increase of users that use the internet in order to communicate and interact, and as a result, the amount of data created follows the same upward trend making data handling ov ...
Students Perception on the Level of Classroom Engagement at a Korean University

This research aims to provide valuable information to the professors and school officials among the higher education institutions in South Korea on the level of classroom engagement among undergraduate students. Questionnaires based on the literature review were administered to 500 students at a university in South Korea. Descriptive statistics, t-test, and ANOVA were used to analyze the data gathered. It was found out that student engagement is the highest in terms of behavioral indicators. Specifically, the responses showed that asking relevant questions during the class is the most frequent way to show that the students are engaged in the classroom. Furthermore, hypotheses, as regards the variance of responses when grouped according to the demographic variables of the respondents, were tested. It was found out that student engagement did not differ significantly in terms of the gender of the participants and their grade point average while the opposite is true in terms of the year level and their department. Furthermore, the most favored engagement strategies by the students include providing the purpose of the activity and assisting the students in completing the activity. This set of information would benefit the teachers and other school officials in creating a plan of action that is geared towards promoting better classroom engagement among students. Through a higher level of engagement, not only the students are learning more effectively, but also they are taking more ownership of their learning process, all of which positively contribute to improved student outcomes.
Pelaksanaan Program BIPA dan Hasil Pembelajarannya

The present study identified the learning outcomes of the program of Indonesian for foreigners (BIPA—Bahasa Indonesia untuk Penutur Asing) and the suitability between student learning outcomes and teaching materials of BIPA. The learning process of BIPA was conducted to improve four language competencies including listening, speaking, reading, and writing. Based on the 2016 and 2017 BIPA teaching reports, thirteen teachers assigned to teach BIPA to Thailand, Vietnam, Myanmar, France, and Russia were successfully able to improve the four language competencies of the students. Meanwhile, the suitability between student learning outcomes and teaching materials of BIPA indicated that: 1) sixty seven BIPA teachers were considered good at adjusting the learning to the teaching materials, 2) four BIPA teachers were rated fair in adjusting the learning to the teaching materials, and 3) ten BIPA teachers were considered poor in adjusting the learning to the teaching materials.
Globally Optimal Weighted Fusion Kalman Filter with Colored Measurement Noises

In this paper, the multisensor systems with ARMA coloured measurement noise are converted to those with the same local dynamic model and uncorrelated noises by using the state augmented method. Furthermore, the globally optimal weighted measurement fusion Kalman filter is given. Compared with the existing methods transforming the multisensor systems into those with the same or different local dynamic models and correlated noises, the transformed system models in this paper are applicable to the weighted measurement fusion algorithm. Although the dimension of augmented state is higher than that in the existing references, the computational load and complexity of the whole optimal fusion Kalman filter are better than the existing fusers. The simulation example for a 3-sensors radar tracking system with colored measurement noises shows its effectiveness.
Circuit Quantum Electrodynamic Model of a Resonantly Phase-Matched Josephson Traveling Wave Parametric Amplifier

In recent years Josephson-junction-based parametric amplifiers have been developed for single-photon-level signals in the microwave spectral region, where they found applications in quantum information processing. Typically, Josephson traveling wave parametric amplifiers (JTWPA) have been studied based on classical circuit models. Although this approach shows good agreement with experimental results, it describes the system behavior in the framework of a classical theory, neglecting the quantum nature of the device. A quantum mechanical treatment of the JTWPA was only given in a few recent papers, where [1] derives a Hamiltonian for a chain of unit cells without resonant phase-matching (RPM), and [2] solves the dynamics of the system with and without RPM, also partly considering noise and squeezed state generation. However, our approach differs from [2] in that we give an explicit solution to the resulting nonlinear wave equation for narrow-band signals, starting from discrete chain Hamiltonians with and without RPM. We focus on describing the dynamics of the device in a noiseless quantum setting and present solutions for the governing nonlinear wave equation in Fig. 1. The starting point of this work is the amplifier design as given in [3]. The mathematical description relies on the Hamiltonian of a discrete chain of unit cells, which was derived in [1], and which we extended by a RPM circuit [2]. As the size Δl of a single chain element is relatively small compared to the wavelength, the chain of discrete Josephson elements can be considered in terms of a continuous transmission line.
The Construction and Development of Digital Electronic Technology Course Majoring in Applied Electronic Technology of Advanced Vocational College

Aiming at the specialty of applied electronic technology in higher vocational college, the paper introduces the construction and development of the course of digital electronic technology in three aspects: teaching material, experiment and practical training. The writing and compiling of teaching material have not only emphasized the follow of logical thinking principle and the use of standardized symbols and equations in order to facilitate students to understand and train innovative thinking, but also adopted many design cases to cultivate and exercise the students' design and innovation ability. Three types of experiments including demonstration type, logical function and characteristic verification type, and analysis and design type have been carried out in the experimental teaching activities. The proper projects of practical training had been chosen beforehand, the cooperation and coordination between teacher and students have been paid attention to during the practical training, and the factors affecting the effect of the practical training should be well controlled by the teacher. The practice has shown that the unifying consideration and arrangements to the related contents of classroom teaching, experiment and practical training can help the students to achieve the goal of mastery.
Challenges in Distributed Agile Software Development Environment: A Systematic Literature Review

Due to increasing interest in distributed agile software development, there is a need to systematically review the literature on challenges encountered in the agile software development environment.Using the Systematic Literature Review (SLR) approach, 32 relevant publications, dated between 2013 and 2018 were selected from four electronic databases.Data from these publications were extracted to identify the key challenges across the system development life cycle (SDLC) phases, which essentially are short phases in each agile-based iteration.5 types of key challenges were identified as impacting the SDLC phases; these challenges are Communication, Coordination, Cooperation, Collaboration and Control.In the context of the SLDC phases, the Communication challenge was discussed the most often (79 times, 33%).The least discussed challenges were Cooperation and Collaboration (26 times, 11% each).The 5 challenges occur because of distances which occur in distributed environment.This SLR identified 4 types of distances which contribute to the occurrence of these key challenges -physical, temporal, social-cultural and knowledge/experience.Of the 32 publications, only 4 included research which proposed new solutions to address challenges in agile distributed software development.The authors of this article believe that the findings in this SLR are a resource for future research work to deepen the understanding of and to develop additional solutions to address the challenges in distributed agile software development.
Secure Mining of Association Rules in Distributed Datasets

The arrival of Information Age, with its rapid development of information technology, has provided a wide space for Data Analysis and Mining. Yet growth in this market could be held back by privacy concerns. This paper addresses the problem of secure association rule mining where transactions are distributed across sources. The existing solutions for distributed data (vertical partition and horizontal partition) have high complexity of encryption and incomplete definition of attributes of multiple parties. In this paper, we study how to maintain differential privacy in distributed databases for mining of association rules without revealing each party's raw transactions despite how strong background knowledge the attackers have. We use a intermediate server for data consolidation without assuming it is safe. Our methods offer enhanced privacy against various attacks model. In addition, it is simpler and is significantly more efficient in terms of communication rounds and computation overhead.
Power and Challenges of Big Data: Why Clinical Researchers Should Not Be Ignored

Operative Unit of Anesthesia and Intensive Care, San Martino Hospital, Belluno, Italy The author has no funding or conflicts of interest to disclose.
Arvio : Schleser, Max; Berry Marsha (Ed.by) Mobile Story Making in an Age of Smartphones, Palgrave MacMillan, 2018.

Kirja-arvostelu teoksesta Schleser, Max; Berry Marsha (Ed.by) Mobile Story Making in an Age of Smartphones, Palgrave MacMillan, 2018.
Automated Supervision of Personal Protective Equipment Usage

Abstract The workers of many fields must follow strict safety rules, and not complying with them may put these workers into severe danger. Oil and Gas workers are subject to hazards and accidents in their workplace frequently. The use of personal protective equipment (PPE) is of summary importance to professionals who work with heavy-duty machinery or in unsafe environments, reducing the risk of serious injuries or even death. The workers are supposed to wear PPE, some of which are gloves, hardhat, and Steel-Toed Boots, during their activities. However, PPE is commonly misused or unused, incurring the need for recurrent inspection. There is no guarantee that the safety equipment is being used correctly, safely and continuously. These threats to the safety of the workers are increased significantly when they are working offshore due to either the harsh conditions they might be working on or inherent dangers that O&amp;G workplaces can offer to the workers such as machinery and dangerous areas with risk of collision and accidents. Detecting the lack of PPE can prevent injuries during work. For this purpose, we propose a surveillance system solution to automatically analyze video footage and detect oil and gas (O&amp;G) workers who are not using adequate protective equipment. This project developed a multi-step detection system using Deep Learning techniques in a pipeline for monitoring workers through camera images. Being able to detect violations to the established rules is an important step towards reducing the impact of incidents and accidents. Using computer vision, deep neural networks, and video footage, we created a web solution for analyzing the imagery in real-time and issuing alerts when a violation happens. For this specific domain, we accomplished the best results by using YOLOv3 as a person detector in conjunction with the Xception network for classification. We achieved 98% precision for the classification step and 78% precision for the joint solution (detection and classification steps) while running in real-time in an NVIDIA Titan X GPU.
Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control

A major risk of an automated high-security entrance control is that an authorized person takes an unauthorized person into the secured area. This practice is called "piggybacking". Known systems try to prevent it by using physical barriers combined with sensory or camera based algorithms. In this paper we present a multi-sensor solution for verifying the number of persons that stand within a defined transit area. We use sensors that are installed in the floor to detect feet as well as camera shots taken from above. We propose an image-based approach that uses change detection to extract motion from a sequence of images and classify it by using a convolutional neural network. Our sensor-based approach shows how user interactions can be used to facilitate safe separation. Both methods are computationally efficient so they can be used in embedded systems. In the evaluation, we were able to achieve state-of-the-art results for both approaches individually. Merging both methods sustainably prevents piggybacking, at a BPCER of 7.1%, where bona fide presentations are incorrectly classified as presentation attacks.
A new approach for the recognition of human activities

The evaluation of a patient's functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient's morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognising activity in a smart home. We evaluate our approach by comparing it to a Markov statistical approach and using several performance measures over three datasets. We show how our model achieves significantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92% and accuracy of 68%. The experiments also show a significant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.
Neural Denoising of Ultra-low Dose Mammography

X-ray mammography is commonly used for breast cancer screening. Radiation exposure during mammography restricts the screening frequency and minimal age. Reduction of radiation dose decreases image quality. Image denoising has been recently considered as a way to facilitate dose reduction in mammography without impacting its diagnostic value. We propose a convolutional locally-consistent non-local means (CLC-NLM) algorithm for ultra-low dose mammography denoising. The proposed method achieves powerful denoising while preserving fine details in high resolution mammography. Validation is performed using a dataset of 16 digital mammography cases (4-views each). Since obtaining true low-dose and high-dose mammogram pairs raises regulatory concerns, we applied the X-ray specific and validated method of Veldkamp et al. to simulate 90% dose reduction. The proposed algorithm is shown to compete favorably, both quantitatively and qualitatively, against state-of-the-art neural denoising algorithms. In particular, tiny micro-calcifications are better preserved using the proposed algorithm.
Using Facebook in Blended Learning in Vietnamese Undergraduate Students

Abstract The study investigated the use of Facebook as an educational tool as well as the influence of Facebook integration into blended learning environment on English pronunciation teaching and learning activities. The subjects for the study consisted of 30 students who were the first-year English majors at Thai Nguyen University of Education. They were divided into two groups: the experimental group and the traditional group. Besides, there was a student teacher from the same university to be the subject of the experimental group. A combination of qualitative, quantitative and experimental approaches was employed to collect data and students’ views on this model. This allowed the researcher to get hold of a transformation in students’ pronunciation competence and their attitudes towards a blended format with supporting a social networking site (Facebook). The findings of the study indicate that Facebook retains outstanding features to offer a professional online course for English pronunciation learning, such as live lectures, group discussions, schedule events and etc. Furthermore, Facebook benefits the teachers in managing students’ learning autonomy so that the teacher is likely to grasp the ability of individuals in a short time. However, the use of Facebook still remains obstacles in the path of carrying out English pronunciation activities, and when putting it into blended-learning, the model handles these obstacles and limitations in the current learning and teaching of English pronunciation as well.
A Novel Lattice-Based Ciphertext-Policy Attribute-Based Proxy Re-encryption for Cloud Sharing

Proxy re-encryption plays an important role in cloud sharing. Ciphertext-policy attribute-based proxy re-encryption (CP-ABPRE) scheme supports access control and can convert the ciphertext under an access policy to a ciphertext under another access policy, which is flexible and efficient for cloud sharing. The existing CP-ABPRE schemes are constructed by bilinear pairing or multi-linear maps which are fragile when the post-quantum comes. In this paper, a unidirectional single-hop CP-ABPRE scheme with small size of public parameters was presented by using trapdoor sampling, and proved secure under learning with errors assumption which is widely believed secure in quantum computer attacks.
Automatic Semantic Text Tagging on Historical Lexica by Combining OCR and Typography Classification

When converting historical lexica into electronic form the goal is not only to obtain a high quality OCR result for the text but also to perform a precise automatic recognition of typographical attributes in order to capture the logical structure. For that purpose, we present a method that enables a fine-grained typography classification by training an open source OCR engine both on traditional OCR and typography recognition and show how to map the obtained typography information to the OCR recognized text output. As a test case, we used a German dictionary (Sander's Wörterbuch der Deutschen Sprache) from the 19th century, which comprises a particularly complex semantic function of typography. Despite the very challenging material, we achieved a character error rate below 0.4% and a typography recognition that assigns the correct label to close to 99% of the words. In contrast to many existing methods, our novel approach works with real historical data and can deal with frequent typography changes even within lines.
ContourNet: Salient Local Contour Identification for Blob Detection in Plasma Fusion Simulation Data

We present ContourNet, a deep learning approach to identify salient local isocontours as blobs in large-scale 5D gyrokinetic tokamak simulation data. Blobs—regions of high turbulence that run along the edge wall down toward the diverter and can damage the tokamak—are non-well-defined features but have been empirically localized by isocontours in 2D normalized fluctuating density fields. The key of our study is to train ContourNet to follow the empirical rules to detect blobs over the time-varying simulation data. The architecture of ContourNet is a convolutional neural segmentation network: the inputs are the density field and a rasterized isocontour; the output is a set of isocontour encircling blobs. At the training stage, we feed the network with manually identified isocontours and propagated labels. At the inference stage, we extract isocontours from the segmented blob regions. Results show that our approach can achieve both high accuracy and performance, which enables scientists to understand the blob dynamics influencing the confinement of the plasma.