Capacity Building In Information And Communication Management (ICM) Towards Food Security

This paper addresses capacity strengthening needs in the area of ICM to support food security initiatives. It fully acknowledges that FS is a state of assuring physical availability and economic accessibility to enough food in terms of quantity (amount, distribution, calories), quality (safe, nutritious, balanced) and cultural acceptability for all people at all times for a healthy and active life. It starts by outlining how ICM can support strategies to ensure availability, access, acceptability, adequacy, and agency and it highlights key information needs in each case. A FS Information and Communication Web is developed basing on a generic conceptual framework of determinants of food security. The web delineates information needs that would support strategies to ensure adequacy of food, stability of supply, and access – physical and economical. The paper then articulates capacity strengthening needs in line with the three dimensions or levels of food security: national, community and household. Four case studies: (i) Uganda’s ICT policy and Food Security (ii) Human Resources needs at community level drawing experiences from Africa and Asia (iii) HR Capacity Development Needs in Africa by the IMF (iv) Audio visual and farmer skills in Mali – serve to demonstrate grassroots ICM applications that support food security initiatives, and in each case it points to theme specific capacity strengthening needs. The studies, as a result, demonstrate how enhanced ICM capacity can support food security through: developing suitable ICT policies, empowering communities with ICM knowledge, improving development planning, enhancing agricultural productivity, supporting marketing systems, improving natural resources management and conservation, and through effective execution of early warning systems – all having implications for food security. The paper concludes by presenting a summary of capacity strengthening needs. These range from sensitization of regional and national policy makers, down to technical skills required by data collectors, analysts and information generators, knowledge disseminators and also knowledge users. To achieve the above the paper proposes roles that may be played by governments, NGOs, education sector, research and development institutions, regional and international organizations, and CTA.Capacity Building, Food Security, ICM, Tanzania

Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

A comparison of generative and discriminative appliance recognition models for load monitoring

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model

An Advanced Home ElderCare Service

With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

Downward trends in violence among adolescents in the United States: Evidence from the NSDUH 2002-2014

OBJECTIVE: To examine trends and correlates of fighting and violence among youth from the nation's three largest racial/ethnic groups in the US. METHODS: A population-based study (National Survey on Drug Use and Health, 2002-2014) of youth ages 12-17 (n = 209,393) provided prevalence estimates for fighting, group fighting, and attacks with the intent to harm by race/ethnicity. RESULTS The prevalence of youth fighting and violence decreased significantly for all racial/ethnic groups, dropping from a high of 33.6% in 2003 to a low of 23.7% in 2014, reflecting a 29% decrease in the relative proportion of young people involved in these behaviors. However, we also see a clear severity gradient in which year-by-year point estimates for fighting and violence are consistently highest among African-American youth followed by Hispanic and then non-Hispanic white youth. CONCLUSIONS Among youth in general and across racial/ethnic subgroups, fighting and violence are on the decline but with a stable pattern of disparities in youth involvement in these behaviors

Fall Detection Using Neural Networks

Falls inside of the home is a major concern facing the aging population. Monitoring the home environment to detect a fall can prevent profound consequences due to delayed emergency response. One option to monitor a home environment is to use a camera-based fall detection system. Conceptual designs vary from 3D positional monitoring (multi-camera monitoring) to body position and limb speed classification. Research shows varying degree of success with such concepts when designed with multi-camera setup. However, camera-based systems are inherently intrusive and costly to implement. In this research, we use a sound-based system to detect fall events. Acoustic sensors are used to monitor various sound events and feed a trained machine learning model that makes predictions of a fall events. Audio samples from the sensors are converted to frequency domain images using Mel-Frequency Cepstral Coefficients method. These images are used by a trained convolution neural network to predict a fall. A publicly available dataset of household sounds is used to train the model. Varying the model\u27s complexity, we found an optimal architecture that achieves high performance while being computationally less extensive compared to the other models with similar performance. We deployed this model in a NVIDIA Jetson Nano Developer Kit

Surveying Persons with Disabilities: A Source Guide (Version 1)

As a collaborator with the Cornell Rehabilitation Research and Training Center on Disability Demographics and Statistics, Mathematica Policy Research, Inc. has been working on a project that identifies the strengths and limitations in existing disability data collection in both content and data collection methodology. The intended outcomes of this project include expanding and synthesizing knowledge of best practices and the extent existing data use those practices, informing the development of data enhancement options, and contributing to a more informed use of existing data. In an effort to provide the public with an up-to-date and easily accessible source of research on the methodological issues associated with surveying persons with disabilities, MPR has prepared a Source Guide of material related to this topic. The Source Guide contains 150 abstracts, summaries, and references, followed by a Subject Index, which cross references the sources from the Reference List under various subjects. The Source Guide is viewed as a “living document,” and will be periodically updated

Information Technology and Transport: What Research Needs to be Started Now?.

The ten week period from 9th October to 19th December 1982 was spent as Visiting Fellow at Leeds University at the Institute for Transport Studies; to examine the opportunities for research into the effects of information technology on transport and the interactions between them. This Fellowship was sponsored by the Science and Engineering Research Council (UK) and the Australian Road Research Board with additional support from Oxford Sytematics (Australia). This report reviews the scope for research in this area; with particular emphasis on identifying workable project directions in the Institute for Transport Studies (ITS). Appropriate contacts and related work are given. Topics covered include data acquisition systems (including the potential for hand-held data capture devices; and the use of aural, visual and micro-wave wavelengths in capturing data): data processing and comunications policy appropriate to the Institute' s requirements; the role of knowledge-based systems; and the analysis of the relation between communications and transport activities in respect of time-use and expenditure patterns. A number of the research proposals raised and put to ITS staff during the period are summarised in an Annex to this report (ITS technical Note TN 126). The summaries and texts of a series of seminars given during November-December 1982 at ITS are covered in a companion document (ITS Working Paper WP169)

Collecting Data from Children Ages 9-13

Provides a summary of literature on common methods used to collect data, such as diaries, interviews, observational methods, and surveys. Analyzes age group-specific considerations, advantages, and drawbacks, with tips for improving data quality