Reviewerly/exHarmony huggingface dataset:
GRIIDC: A Tale of One Data Repository's Journey to Become FAIR

The Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) is a multidisciplinary data repository created as a result of the Deepwater Horizon oil spill. Essentially “building a ship while sailing”, GRIIDC developed a repository while researchers were collecting vital post-spill data during a time when data sharing was not a widely accepted practice. Priorities for the data repository during those early years focused on ingesting data and metadata quickly, while working with researchers who had never shared data before. Almost ten years later, GRIIDC has come a long way – improving data curation, training researchers, and developing software that greatly simplified the submission process. While the ease of submission will always be a priority, GRIIDC has shifted the focus of software development and data curation to implement FAIR data principles, which ensure that data are findable, accessible, interoperable, and reusable. The data package review process has improved by increasing the number of subject matter experts who ensure that files are not corrupt, are in non-proprietary formats, contain complete datasets, and are well-documented with ISO-19115-2 metadata. Datasets are assigned a digital object identifier (DOI) and can be searched using GRIIDC’s data discovery tool. GRIIDC is a DataONE member node and is listed in the Registry of Research Data Repositories (re3data), which increases findability and accessibility of data. Datasets have landing pages which display the DOI, links to associated publications, number of downloads, and a suggested citation. Although the GRIIDC data management system currently supports several FAIR data principles, there is always room for improvement. Potential future tasks include adding vocabularies, improving provenance records, and adding additional attributes to metadata. Challenges to accomplishing these goals include funding, limited resources, and program priorities. Despite these challenges, GRIIDC will always continue to evolve to improve the needs of users as well as support the FAIR data movement. Life may not always be fair, but data should be.
MAIN BARRIERS THAT RURAL SCHOOLS ARE FACED WITH IN INTEGRATING ICT IN TEACHING AND LEARNING

Ponte Academic JournalJan 2020, Volume 76, Issue 1 MAIN BARRIERS THAT RURAL SCHOOLS ARE FACED WITH IN INTEGRATING ICT IN TEACHING AND LEARNINGAuthor(s): Thabang Edwin Maruping ,Mthulisi VelempiniJ. Ponte - Jan 2020 - Volume 76 - Issue 1 doi: 10.21506/j.ponte.2020.1.12 Abstract:The implementation of Information Communications Technology(ICT)to enhance and facilitate teaching and learning in education is a challenge to many schools. Understanding the issues regarding encouragement, support, and required infrastructure to achieve ICT integration is a complex exercise. Government sectors and private institutions donate computers and other ICT equipment on regular basis to schools in an endeavour to equip rural schools and to bridge the digital divide in rural schools. However, the rural schools are not utilizing these donated ICT tools (projectors, interactive whiteboards,and related technologies) which is of concern. Hence the study investigated factors that hinder the successful implementation of ICT in rural schools in Rekopantswe Area. Quantitative research methodology was employed in this study and questionnaires were used to gather data from Area manager, circuit managers, subject advisors,Curriculum Coordinators, Principals, Educators, and Learners from different schools in Rekopantswe Area office. Data was coded and prepared for analysis using a Statistical Package for the Social Science (SPSS). The analyses of the results show that most schools do not have an ICT implementation plan. ICT plans and budget needs to be in place in order to address factors that hinders the successful implementation of ICT in rural schools. Download full text:Check if you have access through your login credentials or your institution Username Password
Scalable precondition-aware service composition with SPARQL

Functional service composition builds a plan that fulfills some user-provided goal using available services. The literature describes several approaches for description of services and goals, with different levels of expressiveness, ranging from the ones based only on inputs and outputs to the more expressive logics-based solutions. The former provide better scalability and performance, while the latter allow for increased expressivity in preconditions and effects of both services and goals. The approach proposed in this paper aims to achieve a balance between expressivity and performance of functional service composition. To this end, a graph-based composition algorithm is extended to support preconditions and effects described with a small subset of SPARQL. Experiments compare the performance of this extended algorithm with two state-of-the-art algorithms that support preconditions and effects and demonstrate better scalability. In one case, a maximum speedup of 29 times was achieved for the problems that could be expressed with the SPARQL subset. In another, problems that a state-of-the-art algorithm could not solve after 5 minutes where solved in seconds.
A Step-by-Step Exact Measuring Angle Calibration Applicable for Multi-Detector Stitched Aerial Camera

Nowadays in surveying and mapping applications, the requirement of aerial mapping camera with large image frame is increasing rapidly, for its advantages of great imaging efficiency. Because of difficulty in fabrication of single large detector, stitching of multiple detectors is an approach to constructing large format digital aerial cameras. Also, it is shown in practice that imagery created from multiple images acquired by multi-detector camera system can have similar metric quality, so this sort of camera system will stay in production. As a result, output from multi-center projected images acquired by multiple detectors to an equivalent single center projected virtual image is a core issue, where an accurate calibration is necessary. Exact measuring angle method is a common camera calibration method in the laboratory, and it is a very accurate method. It also performs well when dealing with calibration for long-focal length camera indoors, without limitation of imaging distance. However, it is not applicable to cameras of which the central area is not imaging. This chapter proposes a step-by-step calibration method. Step 1 is to calibrate the central area with traditional exact angle measurement. And step 2 is to establish the image point transformation according to the relative position of various detectors and hence accomplish the calibration of edge area using the result of central area. Through theoretical derivation and simulation, it proves that the proposed method is suitable for multi-detector stitching aerial camera, and it overcomes the shortcomings of impossible measurement for edge area. Also, measurements error caused by optical axis deviation and focal length inconsistency is considered, and its influence on focal length and principle point calculation is analyzed. By deriving the error propagation model of the proposed exact measuring angle method, the assembly accuracy of some key points in the camera system is estimated, which will be a key indicator of accuracy control during the process of camera system production.
The single-image super-resolution method based on the optimization of neural networks

In the conventional single-image super-resolution algorithms, they assume that the sparse coefficients of the low-resolution patches and the corresponding high-resolution patches are the same. However, from our research, it is found that these coefficients are different most of the times. In this paper, the mapping relationship between the low-resolution coefficients and the high-resolution coefficients are learned based on neural networks. In this method, the low-resolution and high-resolution coefficients are first obtained from training images. Then, they are the inputs for a neural network to train this network. Finally, they are used in the reconstruction of the high-resolution image patches. Experimental results show that the proposed method has better performance than the original state-of-the-art algorithms.
Automated salamander recognition using deep neural networks and feature extraction

This paper presents a study conducted to recognize salamanders by using their unique body markings based on images. The detection and matching of unique patterns in a salamander’s body can be complex due variability in individual animals size, shape, orientation and also influence from the external enviornment. While traditional methods require time intensive manual image corrections of the salamanders to achieve accurate recognition, in this work we propose a fully automatic techinque for straigthening. We also propose a matching technique based on the corrected images. The convolutional neural network ResNet50 and dense scale-invariant feature transform (DSIFT) are used for belly pattern localization, and matching for salamander recognition.
Numerical methods for analysis of the rotating point spread function

The rotating point spread function (PSF) is an effective tool with a various applications in the modern optics. In the optical system with the established rotating PSF, a point-like object is imaged as a rotationally asymmetrical diffractive spot and the longitudinal object translation is transformed to the image rotation. The rotating PSF can be implemented to a standard imaging system by a specially designed mask composed of annular apertures with a spiral phase modulation and the resulting rotation effect is achieved by a superposition of generated optical vortices. Obviously, a numerical algorithm enabling evaluation of the PSF angular orientation plays the key role in the digital processing of the detected rotating PSF. The aim of this work is an analysis of selected numerical methods for the rotating point spread function evaluation.
Minimum-Energy Group Scaling of Wavelet Coefficient Quantization for Audio Watermarking

Signal-to-noise ratio (SNR), bit-error-rate (BER), and Perceptual Evaluation of Audio Quality (PEAQ) are commonly utilized performance indexes in measuring the fidelity, robustness, and quality of watermarking algorithms. However, there is a trade-off relationship between audio quality and robustness. This paper aims in proposing an optimization scheme using multi-coefficients quantization in the wavelet domain. A function connecting the multi-coefficients quantization, composed of arbitrary scaling on the lowest DWT coefficients, and the group SNR of these coefficients is derived. Two-stage Lagrange Principle and minimum-energy approach play two essential roles to obtain the optimal scaling factors. To practically evaluate the watermarked audio quality, an objective measurement using the PEAQ is also performed as well. Experimental results confirm that the embedded audio in the proposed method has high SNR and good PEAQ.
Efficient Tree Construction for Hierarchical Representation of Astronomical Images

Hierarchical image representation is multiscale decomposition of images which is proven very useful in automated faint astronomical source detection from optical images. Hierarchical image representation is typically done using tree data structure (i.e. max-tree), whose computational cost of construction highly depends on the resolution and the dynamic range of image. Efficient and fast tree construction is crucial here because the amount of optical imaging data is increasing exponentially, and the upcoming new giant telescopes such as European Extremely Large Telescope (E-ELT) will produce even more images. Here we investigate some of the recent developments in tree construction algorithms and optimization techniques that have significantly increased its computation speed.
How Digital Realities Will Change Professional Reality

Augmented and virtual realities as digital realities will allow: working at a distance; physically handicapped people having professions never thought of before; c-level management managing sites around the world from the office; and saving paper and trees. People will be able to work as in Real Time Multiplayer games. This chapter also refers the importance of IoT to support these technologies based on optical fiber or 5G; the possibility to be used from blue collars with smartphones up to c-level with powerful hardware. The author exposes differences between augmented and virtual realities in professional context, the possibility to use mixed reality with both technologies, and advantages and problems. Examples are given from manufacturing and maintenance industries and for retail business; to make mock-ups and test, to practice activities, to operate and feel real assets and to help on the sale of products.
THE ANALYSIS OF JARGON USED BY WHATSAAP COMMUNITY AMONG TARBIYAH LECTURERS IN UINSU

Jargon used in a community to create an exclusive language by the group and as a manifestation of the existence of a group. The goal of this research is to describe the form, meaning and function of jargon used by the users community WhatsApp among lecturers in Islamic State University of North Sumatra. This study used qualitative descriptive methods. The source of this research is the conversation and dialogue  among  the lecturers through WhatsApp as a media of interaction. Then, the data from conversation in WhatsApp are collected dan analyzed based on the documentation and observation as the advanced technique. From this research, we can conclude as follow: (1) the use of language chat among lecturers is considered the jargon Indonesia, English, and mixed between Indonesia and English, (2) the forms of jargon  used can be classified as abbreviation, acronyms , and phrases, and (3) the meaning of jargon that is formed is not the true meaning of connotation. Keywords : Jargon, chatting, language variation
Automation of Heat Supply in Greenhouse with Usage of the Renewable Heat Sources on the Basis of Fuzzy Logic Simulation Technics

The article is devoted to the use of fuzzy logic for the automated control of heat supply of complex greenhouses with traditional and renewable energy sources. The paper also reviewed aspects for seminars in relevant technical disciplines in engineering education (designing of thermal systems).
An Evaluation Standard and Loss Function Applied to the Semantic Segmentation of Large Depth of Field Pictures

Aiming at the problem that the semantic segmentation algorithm has poor segmentation results in large depth of field (DOF) images, this paper proposed the concept of depth IoU (dIoU) evaluation standard. This concept based on the effective generalized IoU-loss (GIoU-loss) and the instance-level IoU (iIoU) concept proposed by Cityscapes dataset in the field of target detection. This paper introduced the image depth information into the loss function in the semantic segmentation algorithm. By using dIoU as a evaluation standard, the detection effect of the distant object in the DOF picture will get more weight. It solves the problem of weight reduction caused by the far-reaching effect of the distant object in the traditional evaluation standard.
Determining the Question Based on Handwriting-Recognition of Answer Scripts

The most used form of testing even in modern days is pen-and-paper. In a world where digitizing is rapidly overtaking all other means, it is imperative that we modernize our examination techniques. Answers are split based on question numbers and these are corrected by evaluators. In case of errors in splitting due to incorrect marking or scanning, handwriting recognition can be used to recognize the question an answer belongs to. This approach has been dwelled into in this paper. By creating a personalized dataset for each batch of students, this approach seems promising and gives good results.
Human Machine Interaction

Why do we need greater collaboration between man and machine? Tom Austin-Morgan investigates.
Towards Trusted Identities for Swiss Researchers and their Data

In this paper we report on efforts to enhance the Swiss persistent identifier (PID) ecosystem. We will firstly describe the current situation and the need for improvement in order to describe in full detail the steps undertaken to create a Swiss-wide model. A case study was undertaken by using several data sets from the domains of art and design in the context of the ICOPAD project. We will provide a set of recommendations to enable a PID service that could mint Archival Resource Key (ARK) identifiers or a flavour of Research Resource Identifiers (RRIDs) as complement to Digital Object Identifiers (DOIs). We will conclude with some remarks concerning the transferability of this approach to other areas and the requirements for a national hub for PID management in Switzerland.
Project Redefining Recognition: Challenging traditional research assessment

In our current system, research assessment and reward are weighted heavily towards scientific inputs and outputs, and research impact is measured in funding dollars awarded and research articles produced. The value of science, however, extends well beyond contributions to the research activity itself. ‘Project Redefining Recognition’ is a cross-society initiative which seeks to develop a shared future vision and clear set of actions across the Earth, Space, and Environmental Sciences to expand research assessment to explicitly include, recognize, and reward actions that advance open science, broad communication and public engagement.This presentation will look closely at the ways in which Society partners participating in ‘Project Redefining Recognition’ are supporting their communities in sharing and applying their science, and the challenges and opportunities in our current system to recognize and reward those efforts.
A NOVEL CONVOLUTIONAL AUTOENCODERS BASED MULTILEVEL DENOISING FRAMEWORK FOR MEDICAL IMAGE DATA

Convolutional autoencoders based image denoising has emerged as a promising approach in medical imaging applications. Most of the traditional denoising methods such as Bayesian filter, non-linear median filter, wavelet based shearlet transform etc, are difficult to process medical images due to the existence of additive, multiplicative and Gaussian noise. Also, these models cannot resolve the issue of sparsity in the compressed medical images. To overcome these issues, a novelcompressive sensing based multilevel denoising technique is proposed to minimize the noise rate of medical images in the Convolution neural network(CNN) framework. This technique is implemented in convolutional autoencoders framework to reduce the noise in each deep network layer and to improve the quality of the images. This technique efficiently denoise the medical images using the inter, intra variance in the CNN framework. Experimental results demonstrated that the proposed framework has better PSNR and SSIM measures compared to the traditional state-of-the-art approaches.
Beyond Wisdom of Crowds: Deep Uncertainty Coding for Apparent Age Estimation

Conventional algorithms for apparent age estimation make a strong assumption that the wisdom of crowds (i.e. the mean value of inconsistently annotated age labels) is superior to any individual. In this paper, we go beyond such an assumption and instead of averaging to remove label uncertainty we cope with label uncertainty in training by introducing a new concept of uncertainty coding. Uncertainty coding encodes multiple unary apparent age labels into a binary vector indicating age range (uncertainty) in the target space. We traverse through convolutional features and architecture to learn deep uncertainty mapping, whose predicted codes as mid-level features are fed into the second layer regressor that outputs a target prediction. We conduct experiments on the ChaLearn and FG-NET benchmarks to demonstrate superior performance of the proposed deep uncertainty coding to the state-of-the-arts.
Pemanfaatan Program Microsoft Mathematic Dan Geogebra Untuk Peningkatan Mutu Guru Matematika Tingkat SMP Di Kabupaten Bantaeng Sulawesi Selatan

outcomes generated from this activity are as follows: 1) Enhance the ability of Partners in developing existing technology, especially computers in optimizing learning in the classroom through the Geogebra and Microsoft Mathematic programs. Middle School Mathematics Teachers in Bantaeng District are trained in making teaching materials, especially computer-based self-learning modules that are not only material and questions but must be able to develop children's abilities in constructing the concept of the material being taught. The Geogebra and Microsoft Mathematic programs are application programs that can be utilized in dealing with these problems; 2) Training for teachers who are members of the Bantaeng District Junior High School Mathematics Teacher (MGMP). The training material was in the form of a guidebook for the use of the Geogebra program and the Microsoft Mathematics and its use in mathematics learning developed by the Mathematics Department FMIPA UNHAS dedication team. The teaching materials will be used in the training as an initial stage for the teachers to recognize and master the Geogebra and Microsoft Mathematics programs.
Chaos out of Order: Translations of American and Canadian Contemporary Poetry into Romanian before 1989 from a Complexity Perspective

This essay dwells on Romanian translations of American and Canadian contemporary poetry in stand-alone collections and anthologies between World War II and 1989 against a complexity theory background that sets out to recognize irregularities (or chaotic phenomena) within what is otherwise commonly perceived as an orderly, predictive literary system. Employing a computational network analysis approach, I examine a corpus of such translations that have been typically considered as part and parcel of a heavily controlled cultural system. The analysis shows that a sizeable part of the corpus were translations projects initiated, carried out, published, and promoted by the translators themselves – the result of a series of interactions in interpersonal and transnational networks of private individuals, rather than the result of established institutional policies and publication agendas. The essay also reflects on the need to carry out agent-oriented research in translation studies within the wider context of the digital social humanities, which present both the theoretical framework and the necessary methodologies for describing translators as agents of change.
The Chronological Development of Korean Literacy and the Appearance of the Korean Poet, Yoon, Dong Ju

This study aims to clarify the general principle of the pattern where traditional conceptual literacy is advanced to the advanced level, focusing on the ability to understand and use spoken and written words by the media of Korean alphabet phonemes and characters. And this study analyzes the contribution of a poet Yoon, Dong Ju to raise the level of aesthetic literacy of the Korean discourse community. To this end, it will first identify the general principles of literacy development, and classify the rhetoric applied in Yoon, Dong Ju’s poems as Mimesis, Allegory, and Irony. Through this analysis, it aims to reveal the value that intellectuals of the 21st century can gain by improving Korean literacy.
Homomorphic images and shuffle product on probabilistic automata

A probabilistic automaton (pa) is a fuzzy automaton, in which, sum of all fuzzy memberships of the initial states is one and from each state, on an input symbol, the sum of the fuzzy memberships of the transitions is one. In pa, it is shown that, if h : Γ* → Σ* is a morphism and h–1 (λ) = λ, then the image of a recognisable subset of Γ* is a recognisable subset of Σ* and if h is fine, then the inverse image of recognisable subset of Σ* is recognisable. It is also proved that the shuffle product of any two recognisable sets is recognisable.