Reviewerly/exHarmony huggingface dataset:
Fostering jobs, entrepreneurship and capacity development for African youths: An introduction

African Development ReviewVolume 32, Issue S1 p. S1-S4 INTRODUCTION Fostering jobs, entrepreneurship and capacity development for African youths: An introduction Adeleke O. Salami, Corresponding Author Adeleke O. Salami a.salami@afdb.org Macroeconomic Policy, Forecasting & Research Department, African Development Bank Group, Abidjan, Côte d'Ivoire Correspondence Adeleke O. Salami, Macroeconomic Policy, Forecasting & Research Department, African Development Bank Group, Avenue Joseph Anoma, 01 BP 1387 Abidjan, 01, Côte d'Ivoire. Email: a.salami@afdb.orgSearch for more papers by this authorAdamon N. Mukasa, Adamon N. Mukasa Macroeconomic Policy, Forecasting & Research Department, African Development Bank Group, Abidjan, Côte d'IvoireSearch for more papers by this author Adeleke O. Salami, Corresponding Author Adeleke O. Salami a.salami@afdb.org Macroeconomic Policy, Forecasting & Research Department, African Development Bank Group, Abidjan, Côte d'Ivoire Correspondence Adeleke O. Salami, Macroeconomic Policy, Forecasting & Research Department, African Development Bank Group, Avenue Joseph Anoma, 01 BP 1387 Abidjan, 01, Côte d'Ivoire. Email: a.salami@afdb.orgSearch for more papers by this authorAdamon N. Mukasa, Adamon N. Mukasa Macroeconomic Policy, Forecasting & Research Department, African Development Bank Group, Abidjan, Côte d'IvoireSearch for more papers by this author First published: 04 January 2021 https://doi.org/10.1111/1467-8268.12492Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume32, IssueS1Supplement: Special Issue on Jobs, Entrepreneurship and Capacity Development for African YouthsNovember/Novembre 2020Pages S1-S4 RelatedInformation
PENGEMBANGAN MODUL ELEKTRONIK (E MODUL) KETERAMPILAN BERBAHASA DAN SASTRA INDONESIA SD UNTUK MENINGKATKAN KETERAMPILAN MENYIMAK MAHASISWA PGSD

This development research developed one of the learning media needed during the Covid 19 pandemic, namely an electronic module which is the development of a module that is easier to use, easy to carry, concise and practical by lecturers and students. The module for Elementary School Indonesian Language and Literature Skills for PGSD students was developed so that students can better apply the language skills learning aspects of listening in their ability to synthesize. The research method used 10 steps of Borg and Gall's development research grouped into 4 procedures. The four procedures are. (1) Initial research (2) Product Development, (3) Product testing and revision, and (4) final stage. The data is obtained by analyzing the ratings by users, material experts, and media experts. The result obtained is that the feasibility value by the experts reaches a value of 90% from material experts and 89% from media experts with very valid criteria and is achieved in the second stage. Student response is very good with a score of 74.14%. Pre test results are? and the post test results after using the product being developed are 22,5%. The use of e-modules for learning is proven to improve students' listening skills and shows that this language skills learning module is feasible to use.
Differentiable Programming for Piecewise Polynomial Functions

The paradigm of differentiable programming has considerably enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differentiable programming methods (such as autodiff) typically require that the models be differentiable, limiting their applicability. We introduce a new, principled approach to extend gradient-based optimization to piecewise smooth models, such as k-histograms, splines, and segmentation maps. We derive an accurate form to the weak Jacobian of such functions, and show that it exhibits a block-sparse structure that can be computed implicitly and efficiently. We show that using the redesigned Jacobian leads to improved performance in applications such as denoising with piecewise polynomial regression models, data-free generative model training, and image segmentation.
IMPLEMENTASI MEDIA FLASH CARD: STUDI EKSPERIMENTAL UNTUK KETERAMPILAN BERHITUNG SISWA

Counting skills are an important aspect in learning mathematics. Lack of varied use of media counting in learning, makes students experience boredom in the teaching and learning process. Flash card media is one of the media used to encourage creativity and improve student understanding such as math skills. This study aims to determine whether the average numeracy skills of third grade students in material operations taught by using flash card media is better than students who do not use media at SDN Kebondalem Mojosari, academic year 2019/2020. This research is a quantitative study of experimental design quasi type. Design used is the Posttest-Only Control Design. The study population was all students of SDN Kebondalem Mojosari. Samples were taken by simple random sampling technique so that the class III-A (experimental class) and III-B (control class). Data collection techniques used were tests with instruments consisting of 15 items. Data analysis techniques using the normality test, homogeneity test, balance test, and t test with SPSS 16 assisted. The results showed that the average numeracy skills of third grade students in material operations taught by using flash card media were significantly better than students who did not use media.
Analysis of Ontology Competency Questions and their Formalisations in SPARQL-OWL

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalisations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the myriad of engineering hurdles to using CQs in ontology development. A prerequisite to this is to understand the relation between CQs and the queries over the ontology. We use a new dataset of 234 competency questions and their SPARQL-OWL queries for several ontologies in dierent domains developed by dierent groups, and analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 4-5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the dierent ontologies. Next, we analysed the relation between the found CQ patterns and their respective SPARQL-OWL patterns, which revealed that one CQ pattern may be realised by more than one SPARQL-OWL query pattern, and vice versa. These insights may contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalisation, execution, and general management.
Model-Free Continuous to Discrete Workspace Transformation and Path Planning of a 2DOF Serial Arm for Visual Obstacle Avoidance

In this paper we present our approach on path planning for a 2DOF serial robotic arm without constructing a kinematic model of it. Our approach is different from the standard approach, where you have to know the robot’s kinematic parameters such as length of elements, through the fact that we do not use a kinematic model to position or execute path planning for the arm, but rather rely on visual information from a camera to complete the task. We consider only the positions of the end-effector when planning the path, velocities are ignored. The problem to be solved in this paper is formulated as find a path that positions the robot’s end-effector at the target position and avoids any obstacles. Our approach on solving this problem is to sample the workspace (randomly or by sweeping it incrementally) in order to get a correspondence between the end-effector position to the corresponding joint angle values (equivalent to deriving the kinematic model of the robot, but not through equations) and then use the subset of points as a look-up table to compute a path between the points that take us closest to the target.To prove our concept we designed a system that consists of a 3D printed 2 DOF arm, driven by Dynamixel servos, Videocamera, OpenCV and Python. The precise positioning of the end-effector at the target position is achieved using algorithms presented in the literature and in our previous work.
Towards Robust Graph Neural Networks against Label Noise

Massive labeled data have been used in training deep neural networks, thus label noise has become an important issue therein. Although learning with noisy labels has made great progress on image datasets in recent years, it has not yet been studied in connection with utilizing GNNs to classify graph nodes. In this paper, we proposed a method, named LPM, to address the problem using Label Propagation (LP) and Meta learning. Different from previous methods designed for image datasets, our method is based on a special attribute (label smoothness) of graph-structured data, i.e., neighboring nodes in a graph tend to have the same label. A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label. Experimental results demonstrate that LPM outperforms state-of-the-art methods in graph node classification task with both synthetic and real-world label noise. Source code to reproduce all results will be released.
Question Answering Using Deep Learning

Question answering (QA) is a well-researched problem in NLP. QA application are information retrieval and entity extraction. The paper propose long short-term memory (LSTM) model for text-based question answering where questions are based on a particular sentence. Given a sentences and a question to the model for predict the correct answer. Here question answering using memory network (MN). The memory network including 4 components are input, generalization, output, response. Memory network is the long term memory is required to read a story and then answer the questions. Input is sentences, options and the questions. First passes the question and sentences to the memory model. Using score function assigns a score to each question-sentences pair and get more similar sentences. After the result is matched to the supporting factors using score function. To get the correct answer. Here used the facebook babi dataset.
Omnidirectional Image Data-Set for Computer Vision Applications

In this paper we present an image data-set of different omnidirectional systems. The images include full information of colour, depth, instance segmentation and room layout. This dataset aims to help in the training and test of different neural networks and development of computer vision algorithms.
Combining Pseudo-Point and State Space Approximationsfor Sum-Separable Gaussian Processes

Spatio-temporal Gaussian processes (GPs) are important probabilistic tools for inference and learning in climate science, epidemiology, or any time-driven general GP modelling problem. The current gold-standard methods for scaling GPs to large data sets are various flavours of pseudo-point methods. These methods do not cope well with long or unbounded temporal observation horizons, which undermines their efficiency and effectively turns the computational scaling back to cubic in the number of temporal observations. On the other hand, if the temporal part in the GP prior admits a Markov form, inference can be sped up to linear complexity in the number of temporal observations by using state space models. In this work we show how to combine the most widely used pseudo-point method, Titsias' variational approach, with the state space approximation framework. Our approach hinges on a surprising conditional independence property which applies to space--time separable GPs. We demonstrate empirically that, by utilising pseudo-point approximations over space and state space approximations through time, we are able to construct an approximation that is more scalable and widely applicable to spatio-temporal problems than either method on its own.
An Information Retrieval System Based on Multiple Portlets

Information retrieval systems built with a service-oriented architecture have numerous advantages, and portlets are a key technology to implement services which interact with each other in the presentation layer. This work presents an efficient approach for the communication between the components of an information retrieval system based on multiple portlets in a single user interface. It also presents the architecture and the main methods of the system implemented as a case of use for this approach. It is shown that the proposed solution yields the best inter-portlet communication mechanism in each situation, while possessing the ability to deliver aggregated search and superior user experience.
Application of vikor (vise kriterijumska optimizacija i kompromisno resenje) method for identifying amplifier damage

Abstract In this era, many electronics have been marketed in Indonesia. One of which is amplifier that is used to amplify the sound of MP3 player, radio, VCD or television. An amplifier is an electronic appliance that obviously has a certain lifespan or lifetime and afterwards damage will occur. Most people go to an electronics handyman to repair the damaged electronic appliance but not all handymen can analyze the damage easily. To overcome this problem, a system was setup to identify the damage of an amplifier. A damage identification on this system applied Vikor method. Vikor is a ranking method using multiple criteria ranking index based on certain measurement closed to the ideal solution. This application cloud be used as supporting consideration in talking the required steps for repairing an amplifier. The test result and calculation analysis using Vikor method obtained the system accuracy of 81.25%.
La didáctica digital pre-pandémica. Punto de partida para una transformación educativa en la enseñanza superior

The digitization of societies shows how technology has become an engine of social transformation. This digitization has affected the classrooms and the educational system. The objective of this study is to inquire about the digital transformation that is taking place in the university environment; to know the digital tools that students use, both inside and outside the educational environment, and that can be implemented in the classroom, as well as their perception of technology-mediated learning. The information has been collected a few months before the pandemic situation of COVID-19, so it supposes a description of the university pedagogical reality, in which the weak points of the system are highlighted and it points to lines of work for a digital conversion What a response to this situation.This case study, carried out with a mixed methodology, shows that the frequent use, during the last decades, of digital tools in the classroom has not meant a comprehensive adaptation of higher education to new needs. This diagnosis points out lines of work in which to implement transformations that allow adapting teaching methodologies to these post-pandemic demands.
Research on consensus algorithm of multi energy interaction agents based on PBFT

The flexible flow of renewable energy in multi energy system is an important means to make full use of renewable energy. However, the realization of multi energy system collaborative optimization faces many obstacles at the technical and institutional level. The authenticity of multi energy data, the distributed decision-making of complex system, and the trust between interaction subjects make it impossible for all interaction subjects to reach an effective consensus. As a decentralized and intelligent emerging technology, blockchain has great technical compatibility with the energy Internet, which can provide effective solutions for the collaborative optimization of the energy Internet and realize the profound transformation of the energy Internet. This paper compares and analyzes common consensus mechanisms in blockchain, and proposes a consensus method based on Practical Byzantine fault tolerance (PBFT). In this paper, we use Java programming to simulate a blockchain network composed of four nodes. The experimental results show that the consensus method can ensure the effective consensus between the multi energy interaction agents in the case of node downtime.
An Investigation of Multilingual TDNN-BLSTM Acoustic Modeling for Hindi Speech Recognition

Background: In India, thousands of languages or dialects are in use. Most Indian dialects are low asset dialects. A well-performing Automatic Speech Recognition (ASR) system for Indian languages is unavailable due to a lack of resources. Hindi is one of them as large vocabulary Hindi speech datasets are not freely available. We have only a few hours of transcribed Hindi speech dataset. There is a lot of time and money involved in creating a well-transcribed speech dataset. Thus, developing a real-time ASR system with a few hours of the training dataset is the most challenging task. The different techniques like data augmentation, semi-supervised training, multilingual architecture, and transfer learning, have been reported in the past to tackle the fewer speech data issues. In this paper, we examine the effect of multilingual acoustic modeling in ASR systems for the Hindi language. Objective: This article’s objective is to develop a high accuracy Hindi ASR system with a reasonable computational load and high accuracy using a few hours of training data. Method: To achieve this goal we used Multilingual training with Time Delay Neural Network- Bidirectional Long Short Term Memory (TDNN-BLSTM) acoustic modeling. Multilingual acoustic modeling has significantly improved the ASR system's performance for low and limited resource languages. The common practice is to train the acoustic model by merging data from similar languages. In this work, we use three Indian languages, namely Hindi, Marathi, and Bengali. Hindi with 2.5 hours of training data and Marathi with 5.5 hours of training data and Bengali with 28.5 hours of transcribed data, was used in this work to train the proposed model. Results: The Kaldi toolkit was used to perform all the experiments. The paper is investigated over three main points. First, we present the monolingual ASR system using various Neural Network (NN) based acoustic models. Second, we show that Recurrent Neural Network (RNN) language modeling helps to improve the ASR performance further. Finally, we show that a multilingual ASR system significantly reduces the Word Error Rate (WER) (absolute 2% WER reduction for Hindi and 3% for the Marathi language). In all the three languages, the proposed TDNN-BLSTM-A multilingual acoustic models help to get the lowest WER. Conclusion: The multilingual hybrid TDNN-BLSTM-A architecture shows a 13.67% relative improvement over the monolingual Hindi ASR system. The best WER of 8.65% was recorded for Hindi ASR. For Marathi and Bengali, the proposed TDNN-BLSTM-A acoustic model reports the best WER of 30.40% and 10.85%.
Remainder Quotient Double Hashing Technique in Closed Hashing Search Process

Searching is one of the most important process in many activities to access the data or elements. It can be done both in online and offline mode. Many algorithms are used in data structure to perform search process. Hash search algorithm is one of them which are independent of the number of elements inserted into the table. The aim of this research paper is to study about double hashing method of hash search algorithm for minimizing the collisions during insertion and searching of elements into a table. The results of this research reveal the fact that the searching through double hashing method performs efficient and quicker searching among all other existing searching methods. All these analysis gives an idea to search the elements into two tables i.e main table and collision table. By dividing the elements into two tables, the searching method is anticipated to be quicker than the elements accumulated in one table as well as collisions can also be avoided.
Energy-aware key management and access control for the Internet of things

The need to establish a secure communication for most applications of Internet of Things (IoT) has become increasingly crucial. Nevertheless, one of the major issues of such networks is how to establish cryptographic keys between all IoT objects to ensure secure data exchange. Several key management schemes have been suggested in the literature to achieve this goal, but they must be revised and innovated, while taking into account the limited resources of IoT objects. Likewise, the IoT presents a system where objects belonging to the physical world, are connected to the Internet, and have the capacity to measure, communicate, and act around all over the world. Nevertheless, some information available to IoT objects is private, hence the need to ensure an access control whose aim is to guarantee that the information be accessible only to those whose the access is allowed. In this paper, we propose a scheme involving two basic modules, namely key management, and access control. The key management module is designed to ensure both the generation and storage of symmetric keys for IoT objects in a completely distributed manner without resorting to a central authority. In contrast, the access control module is used to impose an access control policy so as to prevent unauthorized access to services provided by a particular IoT object. The latter is ensured thanks to the existing cooperation between the TGOs servers of different domains in the IoT. Through assessments based on security analysis, we demonstrate that the proposed scheme is more secure than the existing ones. Simulations were also performed to validate the proposed solution by comparing it with some relevant concurrent schemes. The obtained results are not only encouraging, but also very favorable for the proposed scheme.
Genuine secret-sharing states

Quantum secret sharing allows each player to have classical information for secret sharing in quantum mechanical ways. In this work, we construct a class of quantum states on which players can quantumly perform secret sharing secure against dishonest players as well as eavesdropper. We here call them the genuine secret-sharing states. In addition, we show that if $N$ players share an $N$-party genuine secret-sharing state, then arbitrary $M$ players out of the total players can share an $M$-party genuine secret-sharing state by means of local operations and classical communication on the state. We also define the distillable rate with respect to the genuine secret-sharing state, and explain the connection between the distillable rate and the relative entropy of entanglement.