Reviewerly/exHarmony huggingface dataset:
Causal Relationship between Economic Growth and Agricultural productivity in Sub Saharan Africa: A Panel Cointegration Approach

The study revisits the causal relationship between agricultural productivity and economic growth in sub-Saharan Africa. The analysis is based on the panel cointegration approach, estimated using the Pooled Mean Group (PMG) estimators. The study employs a cross-country balanced panel data covering 35 countries from 1981 to 2010. Per capita, gross domestic product is an indicator of economic growth, and the total factor productivity (TFP) index is an agricultural productivity indicator used in the study. The empirical results show the variables have a different integration order based on the unit root test, while evidence of a cointegration relationship among the variables exists. The estimated PMG shows that in the long and short-run, agricultural TFP has significant positive and negative effects on economic growth, respectively, in the study. There is no effect of economic growth on agricultural TFP either in the long and short run. While the causality test shows that agricultural TFP Granger causes economic growth in the long and short run, we found no evidence that economic growth Granger causes agricultural TFP in the short run except in the long run. These findings show that greater attention to improving agricultural TFP would increase economic growth in the region.
Applications of NSST enhancement based on adaptive longicorn optimized segmentation and improved modulus in infrared image of electrical equipment

The normal state of power equipment is directly related to the operation of the system. At present, the most widely used method is to use infrared image to implement real-time monitoring of the operation status of power equipment. In order to solve the problems of noise, blur and low contrast in infrared detection, an infrared image NSST enhancement algorithm based on adaptive segmentation and improved fuzzy enhancement is designed in this paper. The original infrared image is transformed into high-frequency and low-frequency components in NSST domain. Then, the high-frequency component with noise is denoised by the spatial adaptive noise smoothing algorithm, and the improved fuzzy enhancement is used.The low-frequency component with the main body of power equipment is segmented into background and foreground parts by adaptive Longhorn, and then enhanced separately. Finally, the enhanced high-frequency and low-frequency components are inversely transformed by NSST to form the final enhanced image. Compared with the other three traditional algorithms, this algorithm has the advantages: it can not only filter the infrared image noise of power equipment effectively, but also improve the infrared image contrast, making the infrared image conform to the human visual effect, and it is easier for the human eye to recognize the fault. It is very helpful to detect and locate the thermal fault of power equipment.
Development of an Ontology-based Approach for Knowledge Management in Software Testing

Software development organizations are seeking to add quality to their products. Testing processes are strategic elements to manage projects and product quality. However, advances in technology and the emergence of increasingly critical applications make testing a complex task and large volumes of information are generated. In fact, software testing is a knowledge intensive process. Because of this, these organizations have shown a growing interest in Knowledge Management (KM) programs, which in turn support the improvement of testing procedures. KM emerges as a means to manage testing knowledge, and, consequently, to improve software quality. However, there are only a few KM solutions supporting software testing. This paper reports experiences from the development of an approach, called Ontology-based Testing Knowledge Management (OntoT-KM), that aims to assist in launching KM initiatives in the software testing domain with the support of Knowledge Management Systems (KMSs). OntoT-KM provides a process guiding how to start applying KM in software testing. OntoT-KM is based on the findings of a systematic mapping on KM in software testing and the results of a survey with testing practitioners. Moreover, OntoT-KM considers the conceptualization established by a Reference Ontology on Software Testing (ROoST). As a proof of concept, OntoT-KM was applied to develop a KMS called Testing KM Portal (TKMP), which was evaluated in terms of usefulness, usability, and functional correctness. Results show that the developed KMS from OntoT-KM is a potential system for managing knowledge in software testing, so, the approach can guide KM initiatives in software testing.
Developing Hierarchies for Image Classification Model Evaluation

Classes within computer vision (CV) datasets often exhibit hierarchical structures such as super-subordinate IS-A relations. While some common performance metrics for evaluating CV models such as “top-5 error” ignore hierarchical structure, metrics for hierarchical scoring exist, yet effectiveness for meaningful evaluation is dependent on the ability of the hierarchy to reflect important semantic relationships between classes. Most hierarchical scoring methods reward closeness between prediction and ground truth classes. Such schemes may produce the same score when a child is misclassified as a terrorist as when a car is misclassified as a vehicle or helicopter, ignorant of the different levels of impact of these misclassifications.An approach for developing context-aware hierarchies for use with existing evaluation metrics to reflect the cost of misclassification is needed. The contribution of this paper is to provide a hierarchy construction framework that penalizes misclassifications accordingly given a list of importance ordered categories and a hierarchical scoring method. The framework is demonstrated in a hierarchy selection use case and compared quantitatively against the “top-5 error” metric and a simple super-subordinate relation hierarchical scoring. We qualitatively discuss the efficacy and implications of each approach.
Living the POSI Life

Inclusive and efficient open research depends on foundational open scholarly infrastructure. It has become increasingly clear that there is a class of shared infrastructure to enable open research that should be open, community-governed, sustainable and trusted by the research community. However, to date there has been little clarity about how to assess, or even define, open scholarly infrastructure. As services that the scholarly community relies on and are essential to open research have been closed down or sold, it is imperative to understand and assess what constitutes open scholarly infrastructure. &#x0D; The Principles of Open Scholarly Infrastructure (POSI) were conceived to ensure that the stakeholders of a community-led organization or initiative have a clear say in setting its agenda and priorities, and can carefully close it down and start an alternative if needed. &#x0D; Join us in conversation with Ed Pentz, Executive Director of Crossref, to find out why the adoption of POSI is so significant for Crossref, how the organization currently meets the principles and what we will strive to do better, what this will mean for the future of Crossref and the wider community, and how you can get involved and learn more.
Differentially private regression and classification with sparse Gaussian processes

A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable privacy guarantees of differential privacy which has been combined with Gaussian processes through the previously published \emph{cloaking method}. In this paper we solve several shortcomings of this method, starting with the problem of predictions in regions with low data density. We experiment with the use of inducing points to provide a sparse approximation and show that these can provide robust differential privacy in outlier areas and at higher dimensions. We then look at classification, and modify the Laplace approximation approach to provide differentially private predictions. We then combine this with the sparse approximation and demonstrate the capability to perform classification in high dimensions. We finally explore the issue of hyperparameter selection and develop a method for their private selection. This paper and associated libraries provide a robust toolkit for combining differential privacy and GPs in a practical manner.
Audit Sistem Informasi pada E-Learning Universitas Negeri Padang Menggunakan Framework COBIT 4.1

This study aims to determine the extent of UNP's e-learning performance, especially for students entering the year 2020 and provide recommendations for improvement management in accordance with the COBIT 4.1 framework, especially in the domain of delivery and support and monitoring and evaluate. Data collection was carried out by distributing questionnaires to students in the 2020 entry year with a sample of 98 respondents. The method used is descriptive with a qualitative approach. This study resulted in the maturity level of UNP e-learning being at level 4, namely managed and measurable where UNP already has a number of indicators or quantitative measures that serve as performance targets and objectives for each application of existing information technology applications.
Identifikasi Isi Informasi Teks Eksposisi pada Majalah Tempo Serta Pemanfaatannya dalam Pembelajaran Bahasa Indonesia SMP Kelas VIII

This study aims to (1) find out what kind of exposition text in Tempo Magazine, (2) determine the content of exposition text information in Tempo Magazine, (3) explain the use of information content in Tempo Magazine in Indonesian Language learning in VIII Middle School. This type of research is descriptive qualitative. The data of this this research are in the form of exposition text, data sources in the form of Tempo Magazine, 29 April-5 May 2019 edition. Data collection techniques in the form of listening and note talking. Data analysis techniques using the method of distribution. The result of this study are (1) the text of exposition in Tempo Magazine as much as 20 texts with the type of exposition text in the form of news, (2) the text of the exposition in Tempo Magazine has a variety of information, for example in text 1 has the contents of information about funding services for UMKM actors, text 2 contains information on public infrastructure for gender mainstreaming, and text 3 contains information on the revitalization of the transmigration model. (3) the use of the content of exposition text information in Tempo Magazine as teaching materials for VIII grade junior high schools based on the 2013 corriculum with KD 3.5 identifying the content of exposition text information in the form of popular scientific articles from newspapers or magazine that are heard or read.
Location Dominance Effect of Implicit Memory in Test Sequences

Implicit memory is a cognitive phenomenon. In specific cognitive processes, individual implicit memory produces a position dominance effect. The location of test sequence refers to the location of a single test item which is determined by the characteristics of the previous test item in a series of test items. The test items have twomain characteristics: signal characteristics and noise characteristics. Different test sequence locations affect the initiation process of implicit memory through the sequence of signal and noise items, resulting in different effects of implicit memory. Neighborhood relationindicates whether or not two test items have common attributes. In the experimental study, the researchers used artificially-constructed colorless, meaningless English strings as experimental materials and used a two-factor mixed design to explore the effect of the relationship between the location of the test sequence and adjacent positions of individual implicit memory. The results show that there are significant differences in the implicit memory level of college students in different grades and in their implicit memory scores under different test sequence positions. The interaction effect between test sequence locations and adjacent relationships is significant. Hence, the location of test sequence and its adjacent position relationship producea position advantage effect by influencing the implicit priming mechanism.Keywords: Recognition Test, Neighboring Position Relationship, Test Sequence Location, Implicit Memory, Dominance EffectCite AsB. Yu, Location Dominance Effect of Implicit Memory in Test Sequences, Engineering Intelligent Systems, vol. 29 no. 5, pp. 339-347, 2021.
ANALISIS VARIATION K-FOLD CROSS VALIDATION ON CLASSIFICATION DATA METHOD K-NEAREST NEIGHBOR

To produce a data classification that has data accuracy or similarity in proximity of a measurement result to the actual numbers or data, testing can be done based on accuracy with test data parameters and training data determined by Cross Validation. Therefore data accuracy is very influential on the final result of data classification because when data accuracy is inaccurate it will affect the percentage of test data grouping and training data. Whereas in the K-Nearest Neighbor method there is no division of training data and test data. For this reason, researchers analyzed the determination of training data and test data using the Cross validation algorithm and K-Nearest Neighbor in data classification. The results of the study are based on the results of the evaluation of the Cross Validation algorithm on the effect of the number of K in the K-nearest Neighbor classification of data. The author tests using variations in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9. While the training and test data distribution using Cross validation uses variations in the number of K-Fold 1,2,3,4,5,6,7,8,9,10
Penggunaan Model Numbered Heads Together Dapat Meningkatkan Hasil Belajar Menjelaskan Bilangan Bulat Negatif Pada Siswa Kelas VI

The purpose of this Classroom Action Research is that the use of the Numbered Heads Together model can improve class VI SDN 2 Sugihan students in the 2019/2020 school year. This research was conducted in two cycles and each cycle consisted of three meetings. The procedure in each cycle includes the following steps: 1) planning, 2) implementation, 3) observation, 4) reflection. The test result data explaining negative integers was analyzed by comparing the test scores between cycles until the results reached the completion limit according to the performance indicators. The action research carried out in two cycles showed that the average pre-cycle was 58.5 completeness 35%, the first cycle was 70 60% complete, the second cycle 80 was 95% complete. Based on the actions taken, it can be concluded: The use of the Numbered Heads Together model can improve learning outcomes to explain negative integers by 60% of VI grade students of SDN 2 Sugihan for the 2019/2020 school year by 35%.
Automatic Learning of Subword Dependent Model Scales

To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior corrections. This is usually done via log-linear model combination using separate scaling parameters for each model. Typically these parameters are manually optimized on some held-out data. In this work we propose to optimize these scaling parameters via automatic differentiation and stochastic gradient decent similar to the neural network model parameters. We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attentionbased encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning. We further extend this approach to subword dependent model scales which could not be tuned manually which leads to 7% improvement on LBS and 3% on SWB. We also show that joint training of scales and model parameters is possible and gives additional 6% improvement on LBS.
(2010-6244) An identifi cation model for a fuzzy time based stationary discrete process

A new approach of fuzzy processes, the source of which are expert knowledge reflections on the states on Stationary Discrete Extremal Fuzzy Dynamic System (SDEFDS) in extremal fuzzy time intervals, are considered. A fuzzy-integral representation of a stationary discrete extremal fuzzy process is given. A method and an algorithm for identifying the transition operator of SDEFDS are developed. The SDEFDS transition operator is restored by means of expert knowledge reflections on the states of SDEFDS. The regularization condition for obtaining of the quasi-optimal estimatorof the transition operator is represented by the theorem. The corresponding calculating algorithm is provided. The results obtained are illustrated by an example in the case of a finite set of SDEFDS states.
IDDA: A Large-Scale Multi-Domain Dataset for Autonomous Driving

Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large scale annotated datasets. This issue has been traditionally circumvented through the use of synthetic datasets, that have become a popular resource in this field. They have been released with the need to develop semantic segmentation algorithms able to close the visual domain shift between the training and test data. Although exacerbated by the use of artificial data, the problem is extremely relevant in this field even when training on real data. Indeed, weather conditions, viewpoint changes and variations in the city appearances can vary considerably from car to car, and even at test time for a single, specific vehicle. How to deal with domain adaptation in semantic segmentation, and how to leverage effectively several different data distributions (source domains) are important research questions in this field. To support work in this direction, this letter contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions, in seven different city types. Extensive benchmark experiments assess the dataset, showcasing open challenges for the current state of the art. The dataset will be available at: https://idda-dataset.github.io/home/ .
Computing Distributed Knowledge as the Greatest Lower Bound of Knowledge

Let \(L\) be a distributive lattice and \({\mathcal {E}}(L)\) be the set of join endomorphisms of \(L\). We consider the problem of finding \(f \sqcap _{{\mathcal {E}}(L)} g\) given \(L\) and \(f,g\in {\mathcal {E}}(L)\) as inputs. (1) We show that it can be solved in time O(n) where \(n=| L|\). The previous upper bound was \(O(n^2)\). (2) We characterize the standard notion of distributed knowledge of a group as the greatest lower bound of the join-endomorphisms representing the knowledge of each member of the group. (3) We show that deciding whether an agent has the distributed knowledge of two other agents can be computed in time \(O(n^2)\) where n is the size of the underlying set of states. (4) For the special case of S5 knowledge, we show that it can be decided in time \(O(n\alpha _{n})\) where \(\alpha _{n}\) is the inverse of the Ackermann function.
NIEARA: Gamified Literacy App for Children with Learning Disabilities

NieAra is a literacy mobile app that holds true to the primary purpose of promoting vocabulary acquisition for children with learning disabilities such as dyslexia. Derived from the Swahili word of “niara”, which means with utmost purpose, NieAra aims to assist children in the process of recognizing letters and words formation. The application is designed and developed based on theoretical foundations of gamification and language acquisition in aiding the learners to learn words effectively beyond the “fun” factor. It uses several localised metaphors in creating suitable learning paths for learners to learn words as they complete well-designed learning missions. As they complete each mission, they are able to learn multiple sets of contextualised words. In addition, the review of previous similar games shows that limited considerations were given in incorporating a learning analytics component. In NieAra, however, parents would be able to track their child’s learning through a visualized map with specific details of the child’s performance in each level. For example, parents can identify which letters their child have problems recognizing. The game will then provide more options for practice with meaningful drills.
College English Online Teaching Practice under the Guidance of Output Oriented Approach

The output oriented method is mainly to realize the cultivation of students’ individuality, and ensure that students can make practical use of the knowledge they have learned. And the output oriented method can also achieve efficient interaction and communication between students and teachers. In the process of teaching practice, it can also effectively consolidate and strengthen teaching for students, so as to improve the quality and efficiency of college English online teaching.
Chapter 3. Knowledge Organization Systems (KOS) in the Semantic Web. A Multi-Dimensional Review

The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.