CGIAR Research Program on Forests, Trees and Agroforestry - Plan of Work and Budget 2020

There were no significant changes in 2019 to FTA’s theory of change1. FTA plans all its work on the basis of its operational priorities. These, in turn, focusresearch towards major development demands and knowledge gaps, orienting FTA towards the implementation of the SDGs and other global commitments. Three operational priorities were added in 2020 (see list in Appendix 1) to better delineate pre-existing research areas addressing development bottlenecks needing dedicated investment and visibility: smallholder tree-crop commodities, tree seeds and seedlings delivery systems, and foresight. FTA organized in 2019, at the request of its ISC, a joint ISC-FTA workshop on impact assessment methods for the program. Based on the outcomes of this workshop FTA will, inter alia, revisit in 2020 its impact pathways and end of programme outcomes, and if need be, corresponding adjustments to the ToC of FTA and/or of its FPs will be made

CGIAR Research Program on Forests, Trees and Agroforestry - Plan of Work and Budget 2020

There were no significant changes in 2019 to FTA’s theory of change1. FTA plans all its work on the basis of its operational priorities. These, in turn, focusresearch towards major development demands and knowledge gaps, orienting FTA towards the implementation of the SDGs and other global commitments. Three operational priorities were added in 2020 (see list in Appendix 1) to better delineate pre-existing research areas addressing development bottlenecks needing dedicated investment and visibility: smallholder tree-crop commodities, tree seeds and seedlings delivery systems, and foresight. FTA organized in 2019, at the request of its ISC, a joint ISC-FTA workshop on impact assessment methods for the program. Based on the outcomes of this workshop FTA will, inter alia, revisit in 2020 its impact pathways and end of programme outcomes, and if need be, corresponding adjustments to the ToC of FTA and/or of its FPs will be made

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar

Android Permissions Remystified: A Field Study on Contextual Integrity

Due to the amount of data that smartphone applications can potentially access, platforms enforce permission systems that allow users to regulate how applications access protected resources. If users are asked to make security decisions too frequently and in benign situations, they may become habituated and approve all future requests without regard for the consequences. If they are asked to make too few security decisions, they may become concerned that the platform is revealing too much sensitive information. To explore this tradeoff, we instrumented the Android platform to collect data regarding how often and under what circumstances smartphone applications are accessing protected resources regulated by permissions. We performed a 36-person field study to explore the notion of "contextual integrity," that is, how often are applications accessing protected resources when users are not expecting it? Based on our collection of 27 million data points and exit interviews with participants, we examine the situations in which users would like the ability to deny applications access to protected resources. We found out that at least 80% of our participants would have preferred to prevent at least one permission request, and overall, they thought that over a third of requests were invasive and desired a mechanism to block them

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars