The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Deliverable D2.1 - Ecosystem analysis and 6G-SANDBOX facility design

This document provides a comprehensive overview of the core aspects of the 6G-SANDBOX project. It outlines the project's vision, objectives, and the Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) targeted for achievement. The functional and non-functional requirements of the 6G-SANDBOX Facility are extensively presented, based on a proposed reference blueprint. A detailed description of the updated reference architecture of the facility is provided, considering the requirements outlined. The document explores the experimentation framework, including the lifecycle of experiments and the methodology for validating KPIs and KVIs. It presents the key technologies and use case enablers towards 6G that will be offered within the trial networks. Each of the platforms constituting the 6G-SANDBOX Facility is described, along with the necessary enhancements to align them with the project's vision in terms of hardware, software updates, and functional improvements

ПІДХОДИ ДО БЕЗПЕКИ ХМАРО-ОРІЄНТОВАНОГО МОБІЛЬНОГО НАВЧАННЯ

The paper attempts to outline the security issues in the development and application of cloud-based mobile learning. A brief definition of the mobile learning, its components and related technologies and devices is given. The specific characteristics of social media, big data and cloud computing are summarized in relation with their integration in the mobile learning and its transformation to a cloud-based environment. The main security threats to this type of learning are pointed out and some recommendations for providing security learning are given are given.В статье делается попытка выделить проблемы безопасности при разработке и применении мобильной учебы с применением облачных технологий. Сделано короткое определение мобильной учебы, ее компонентов, сопутствующих технологий и устройств. Обобщены особенности социальных медиа, больших данных и облачных  технологий  в отношении к их интеграции  в мобильную учебу и трансформацию в облачной среде. Определены основные угрозы для безопасности такого вида учебы и предоставлены некоторые рекомендации для обеспечения безопасности учебы. У статті робиться спроба окреслити проблеми безпеки при розробці та застосуванні мобільного навчання з застосуванням хмарних технологій. Зроблено коротке визначення мобільного навчання, його компонентів, супутніх технологій та пристроїв. Підсумовані особливості соціальних медіа, великих данних та хмарних  технологій  у відношенні до їх інтеграції  у мобільне навчання та трансформацію в хмарному середовищі. Визначені основні загрози для безпеки такого виду навчання та надані деякі рекомендації для забезпечення безпеки навчання

Using Agent Solutions and Visualization Techniques to Manage Cloud-based Education System

Over the past few years, there are many requests from academic institutions, eLearning developers, education businesses owners, and global enterprises concerning cloud-based education systems. Nowadays, a range of software and applications have been created for managing teaching and learning resources via internet. Many of them have been even trying to integrate all the educational resources into a single cloud system. This paper proposes using agent technologies and visualization solutions to manage cloud-based education systems to match streamline of day to day business and operations. It focuses on adopting agents for University of Westminster’s Cloud computing education system and mobile learning project. It shows how intelligent agents can be used as a good tool for cloud-based education service and associated applications provision and management within Software as Service (SaaS) level

based on K-water case

Thesis(Master) --KDI School:Master of Public Management,2020.1. Introduction 2. Big data in water resources sector 3. Big data Policy and Technology Trends 4. Big data in water resources 5. Changes of the Data Usage in the Big Data Era 6. In the Big Data Era, the limitations of traditional systems development and management 7. Development Method and Governance in Big Data System 8. Big Data Operation Case in K-watermaste

Teaching Computer Programming Through Hands-on Labs on Cognitive Computing

In this work we report the experience of a long-lasting educational project that we have been carrying since a couple of years. In particular, we summarize the results achieved by students in the last year, when they were put to work on the collaborative development of small, yet full featured, software projects. At the same time, based on more recent findings, we seek to lay the foundations to build a pragmatic model to teach cognitive computing programming. The experience was carried on in a Programming course at the Universities of Naples “Federico II” and Genoa, in Italy, and fostered the use of a PaaS (Platform as a Service) environment for a cooperative learning activity, used to disseminate theoretical concepts acquired within the course, also by means of cognitive computing tools. The project, from its inception, has involved a relevant number of students. Initially, the experiment had to be concluded in one year but, instead, has continued evolving with new projects, as new tools and services were made available, carrying new opportunities. The evolution has led, in the most recent release, to using the IBM Bluemix platform with its wide range of components, including Watson. This work goes in the direction of developing the smart university model, by using innovative and intelligent services to help develop a new generation of applications, but also to promote and disseminate a new way for designing and building them

Student Advising and Retention Application in Cloud Computing Environment

This paper proposes a new user-friendly application enhancing and expanding the current advising services of Gradesfirst currently being used for advising and retention by the Athletic department of UMES with a view to implement new performance activities like mentoring, tutoring, scheduling, and study hall hours into existing tools. This application includes various measurements that can be used to monitor and improve the performance of the students in the Athletic Department of UMES by monitoring students' weekly study hall hours, and tutoring schedules. It also supervises tutors' login and logout activities in order to monitor their effectiveness, supervises tutor-tutee interaction, and stores and analyzes the overall academic progress of each student. A dedicated server for providing services will be developed at the local site. The paper has been implemented in three steps. The first step involves the creation of an independent cloud computing environment that provides resources such as database creation, query-based statistical data, performance measures activities, and automated support of performance measures such as advising, mentoring, monitoring and tutoring. The second step involves the creation of an application known as Student Advising and Retention (SAR) application in a cloud computing environment. This application has been designed to be a comprehensive database management system which contains relevant data regarding student academic development that supports various strategic advising and monitoring of students. The third step involves the creation of a systematic advising chart and frameworks which help advisors. The paper shows ways of creating the most appropriate advising technique based on the student's academic needs. The proposed application runs in a Windows-based system. As stated above, the proposed application is expected to enhance and expand the current advising service of Gradesfirst tool. A brief demonstration of the proposed application will highlight the main features of the existing tool (Gradefirst)

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

This paper presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system that supports deep learning models for mobile apps. FLSys is a key component toward creating an open ecosystem of FL models and apps that use these models. FLSys is designed to work with mobile sensing data collected on smart phones, balance model performance with resource consumption on the phones, tolerate phone communication failures, and achieve scalability in the cloud. In FLSys, different DL models with different FL aggregation methods in the cloud can be trained and accessed concurrently by different apps. Furthermore, FLSys provides a common API for third-party app developers to train FL models. FLSys is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) in the wild FL model. HAR sensing data was collected in two areas from the phones of 100+ college students during a five-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed (non-IID) data that affects FL model training in the wild. A sentiment analysis (SA) model is used to demonstrate how FLSys effectively supports concurrent models, and it uses a dataset with 46,000+ tweets from 436 users. We conducted extensive experiments on Android phones and emulators showing that FLSys achieves good model utility and practical system performance.Comment: The first two authors contributed equally to this wor

Design and implementation of application-specific medium access control protocol for scalable smart home embedded systems

Thesis (M.S.) University of Alaska Fairbanks, 2016By incorporating electrical devices, appliances and house features in a system that is controlled and monitored either remotely or on-site, smart home technologies have recently gained an increasing popularity. There are several smart home systems already available, ranging from simple on-site home monitoring to self-learning and Wi-Fi enabled systems. However, current systems do not fully make use of recent technological advancement and synergy among a variable number of sensors for improved data collection. For a synergistic system to be provident it needs to be modular and scalable to match exact user needs (type of applications and adequate number of sensors for each application). With an increased number of sensors intelligently placed to optimize the data collection, a wireless network is indispensable for a flexible and inexpensive installation. Such a network requires an efficient medium access control protocol to sustain a reliable system, provide flexibility in design and to achieve lower power consumption. This thesis brings to light practical ways to improve current smart home systems. As the main contribution of this work, we introduce a novel application-specific medium access control protocol able to support suggested improvements. In addition, a smart home prototype system is implemented to evaluate the protocol performance and prove concepts of recommended advances. This thesis covers the design of the proposed novel medium access protocol and the software/hardware implementation of the prototype system focusing on the monitoring and data analysis side, while providing inputs for the control side of the system. The smart home system prototype is Wi-Fi and Web connected, designed and implemented to emphasize system usability and energy efficiency