Lexical acquisition in elementary science classes

The purpose of this study was to further researchers' understanding of lexical acquisition in the beginning primary schoolchild by investigating word learning in small-group elementary science classes. Two experiments were conducted to examine the role of semantic scaffolding (e.g., use of synonymous terms) and physical scaffolding (e.g., pointing to referents) in children's acquisition of novel property terms. Children's lexical knowledge was assessed using multiple tasks (naming, comprehension, and definitional). Children struggled to acquire meanings of adjectives without semantic or physical scaffolding (Experiment 1), but they were successful in acquiring extensive lexical knowledge when offered semantic scaffolding (Experiment 2). Experiment 2 also shows that semantic scaffolding used in combination with physical scaffolding helped children acquire novel adjectives and that children who correctly named pictures of adjectives had acquired definitions

Low carbon housing: lessons from Elm Tree Mews

This report sets out the findings from a low carbon housing trial at Elm Tree Mews, York, and discusses the technical and policy issues that arise from it. The Government has set an ambitious target for all new housing to be zero carbon by 2016. With the application of good insulation, improved efficiencies and renewable energy, this is theoretically possible. However, there is growing concern that, in practice, even existing carbon standards are not being achieved and that this performance gap has the potential to undermine zero carbon housing policy. The report seeks to address these concerns through the detailed evaluation of a low carbon development at Elm Tree Mews. The report: * evaluates the energy/carbon performance of the dwellings prior to occupation and in use; * analyses the procurement, design and construction processes that give rise to the performance achieved; * explores the resident experience; * draws out lessons for the development of zero carbon housing and the implications for government policy; and * proposes a programme for change, designed to close the performance gap

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

Workshop for annual review of Building Resilient Agro-sylvopastoral Systems in West Africa through Participatory Action Research (BRAS-PAR) Project and planning “Partnerships for Scaling Climate-Smart Agriculture (P4S) Phase II

Building Resilient Agro-sylvo-pastoral Systems in West Africa through Participatory Action Research (BRAS-PAR) is a CCAFS Flagship 2 funded four year (2015-2018) project coordinated by the World Agroforestry (ICRAF) and implemented in collaboration with partners namely national agricultural research institutions (INERA in Burkina Faso, SARI in Ghana, INRAN in Niger and ISRA in Senegal) and the International Union for Conservation of Nature (IUCN in Burkina Faso). BRAS-PAR sought to develop up-scalable technological and social innovations of climatesmart agriculture integrating tree-crop-livestock systems through improved understanding of farmer's perceptions and demands, by addressing barriers to adoption taking into consideration gender and social differentiation. The specific objectives include 1) testing, evaluating and validating with rural communities and other stakeholders, scalable climate-smart models of integrated tree-crop-livestock systems, the dominant farming systems in the region, that include climate-risk management strategies; 2) simulating options for improving water and tree-crop-livestock systems under different climate and socio-economic scenarios using models (WaNuLCAS, SWAT, etc.) for informed decision making; 3) assessing the conditions of success and failure of technological interventions on adaptation to climate change. The work here focus on research that evaluates climate-smart practices and technologies that are defined through participatory identification by multistakeholders in each site. Beyond these sites, the approach capitalizes lessons learnt from on-going climate resilient projects to encourage partners to add missing components to the climate-smart village model or initiate new activities when deemed appropriate. Started in 2015, BRAS-PAR targeted three main outcomes: (i) National agricultural research institutions institutionalize the principles of PAR through integration of non-traditional partners in technologies development to generate wider context specific information to be fed into programs and policies to create the enabling environment for the scaling of CSA technologies; (ii) National extension services, development projects and farmer’s organizations widely disseminate and ensure better access to information on best fit CSA portfolios to cope with climate change; and (iii) The private sector including NGOs (FNGN, Larwaal, ARCAD, Care international), microcredit institutions, agro-dealers, rural radios are scaling up/out relevant CSA portfolios through new incentive programs. This project has ended in December 2018 and the meeting review edthe main achievements. During the same first phase of CCAFS , the project “Partnerships for Scaling (P4S) Climate-Smart Agriculture (P56)” was implemented mainly in East Africa with a focus on supporting countries and partners to plan and program CSA actions. It developed new innovations (e.g., The Compendium and Climate Risk Profiles), refreshed and adapted others (e.g., Climate Wizard, mobile-based monitoring) and collaborated on tools (e.g., Rural Household Multi-Indicator Survey, CSA MRV Profile) to develop a comprehensive set of evidence and information to serve diverse stakeholder needs for situation analysis, targeting and prioritizing, program support and monitoring and evaluation (aka ‘CSA-Plan’, Girvetz et al. 2018). Merging the actions of BRAS-PAR and P4S I to become P4S II was done with the intention to use tools and evidence/lessons learned from the Climate-Smart Villages and other development activities, with existing and new partners through direct scientific support to decision makers (e.g., governments, civil society, and researchers) and capacity building to help bring CSA to scale. The scientific activities will be combined with dedicated communication activities such as photo essays, tweets, blog posts, etc. from field staff and partners to raise the visibility of the project and help show case of its successes in supporting countries and position of ICRAF, CIAT, and CCAFS as the go to research organization for the science of scaling up CSA. The key activity areas of P4S II will be around: supporting CSA investment and programming, de-risking agriculture, digital delivery and monitoring and, communauty based scaling of CSA. The present meeting was thought to plan the new activities around these areas for 2019 and beyond

Scalability of Genetic Programming and Probabilistic Incremental Program Evolution

This paper discusses scalability of standard genetic programming (GP) and the probabilistic incremental program evolution (PIPE). To investigate the need for both effective mixing and linkage learning, two test problems are considered: ORDER problem, which is rather easy for any recombination-based GP, and TRAP or the deceptive trap problem, which requires the algorithm to learn interactions among subsets of terminals. The scalability results show that both GP and PIPE scale up polynomially with problem size on the simple ORDER problem, but they both scale up exponentially on the deceptive problem. This indicates that while standard recombination is sufficient when no interactions need to be considered, for some problems linkage learning is necessary. These results are in agreement with the lessons learned in the domain of binary-string genetic algorithms (GAs). Furthermore, the paper investigates the effects of introducing utnnecessary and irrelevant primitives on the performance of GP and PIPE.Comment: Submitted to GECCO-200

An informal evaluation of a planned phonics program in grade one

Thesis (Ed.M.)--Boston Universit

Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning

The central problem in automated planning---action selection---is also a primary topic in the dialogue systems research community, however, the nature of research in that community is significantly different from that of planning, with a focus on end-to-end systems and user evaluations. In particular, numerous toolkits are available for developing speech-based dialogue systems that include not only a method for representing states and actions, but also a mechanism for reasoning and selecting the actions, often combined with a technical framework designed to simplify the task of creating end-to-end systems. We contrast this situation with that of automated planning, and argue that the dialogue systems community could benefit from some of the directions adopted by the planning community, and that there also exist opportunities and lessons for automated planning

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector