Are interventions for improving the quality of services provided by specialized drug shops effective in sub-Saharan Africa? A systematic review of the literature.

PURPOSE: We set out to determine effectiveness of interventions for improving the quality of services provided by specialized drug shops in sub-Saharan Africa. DATA SOURCES: We searched PubMed, CAB Abstracts, Web of Science, PsycINFO and Eldis databases and websites for organizations such as WHO and Management Sciences for Health. Finally, we searched manually through the references of retrieved articles. STUDY SELECTION: Our search strategy included randomized trials, time-series studies and before and after studies evaluating six interventions; education, peer review, reorganizing administrative structures, incentives, regulation and legislation. DATA EXTRACTION: We extracted information on design features, participants, interventions and outcomes assessed studies for methodological quality, and extracted results, all using uniform checklists. RESULTS OF DATA SYNTHESIS: We obtained 10 studies, all implementing educational interventions. Outcome measures were heterogeneous and included knowledge, communication and dispensing practices. Education improved knowledge across studies, but gave mixed results on communication between sellers and clients, dispensing of appropriate treatments and referring of patients to health facilities. Profit incentives appeared to constrain behaviour change in certain instances, although cases of shops adopting practices at the expense of sales revenue were also reported. CONCLUSION: Evidence suggests that knowledge and practices of pharmacies and drug shops can be improved across a range of diseases and countries/regions, although variations were reported across studies. Profit incentives appear to bear some influence on the level of success of interventions. More work is required to extend the geographical base of evidence, investigate cost-effectiveness and evaluate sustainability of interventions over periods longer than 1 year

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale

A User-Centered Concept Mining System for Query and Document Understanding at Tencent

Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy are core research problems in text understanding and support many downstream tasks such as query analysis, knowledge base construction, recommendation, and search. However, we argue that most prior studies extract formal and overly general concepts from Wikipedia or static web pages, which are not representing the user perspective. In this paper, we describe our experience of implementing and deploying ConcepT in Tencent QQ Browser. It discovers user-centered concepts at the right granularity conforming to user interests, by mining a large amount of user queries and interactive search click logs. The extracted concepts have the proper granularity, are consistent with user language styles and are dynamically updated. We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser. We performed extensive offline evaluation to demonstrate that our approach could extract concepts of higher quality compared to several other existing methods. Our system has been deployed in Tencent QQ Browser. Results from online A/B testing involving a large number of real users suggest that the Impression Efficiency of feeds users increased by 6.01% after incorporating the user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201