Evaluating Rural Farmers Knowledge, Perception, and Adaptation Strategies on Climate Change in Ghana: A case study of the Wa West District, Ghana

Climate change is a phenomenon that has received significant international attention over the past years due to its profound negative effects on community livelihood especially in developing countries like Ghana where rain-fed agriculture is the main source of employment and livelihood for the majority of the population. Various studies have shown that knowledge and perceptions of people on climate change can have a significant influence on their adaptation and mitigation options, it is vital for researchers to, therefore, undertake regular assessments to gather concrete information on climatic trends and its impact so as to prescribe the best mitigative remedies. This work, therefore, evaluates the perceptions of farmers, their level of knowledge on climate change and the various strategies they employ in Wa West District of the Upper West region of Ghana. Based on findings from this study, a fervent recommendation for the building of resilience interventions in the study area that will target farmers especially women with low adaptive capacity to help boost their capacity in dealing with climate change was proposed. Keywords: Adaptation strategies, Barriers, Climate Change, Knowledge, Rural farmers

Adaptive Channel Recommendation For Opportunistic Spectrum Access

We propose a dynamic spectrum access scheme where secondary users recommend "good" channels to each other and access accordingly. We formulate the problem as an average reward based Markov decision process. We show the existence of the optimal stationary spectrum access policy, and explore its structure properties in two asymptotic cases. Since the action space of the Markov decision process is continuous, it is difficult to find the optimal policy by simply discretizing the action space and use the policy iteration, value iteration, or Q-learning methods. Instead, we propose a new algorithm based on the Model Reference Adaptive Search method, and prove its convergence to the optimal policy. Numerical results show that the proposed algorithms achieve up to 18% and 100% performance improvement than the static channel recommendation scheme in homogeneous and heterogeneous channel environments, respectively, and is more robust to channel dynamics

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the \mbox{Tornado} framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our \mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure

An Independent Review of USGS Circular 1370: An Evaluation of the Science Needs to Inform Decisions on Outer Continental Shelf Energy Development in the Chukchi and Beaufort Seas, Alaska

Reviews the U.S. Geological Survey's findings and recommendations on Alaska's Arctic Ocean, including geology, ecology and subsistence, effect of climate change on, and impact of oil spills. Makes recommendations for data management and other issues

Adaptive Hypermedia made simple using HTML/XML Style Sheet Selectors

This paper addresses enhancing HTML and XML with adaptation functionalities. The approach consists in using the path selectors of the HTML and XML style sheet languages CSS and XSLT for expressing content and navigation adaptation. Thus, the necessary extensions of the selector languages are minimal (a few additional constructs suffice), the processors of these languages can be kept almost unchanged, and no new algorithms are needed. In addition, XML is used for expressing the user model data like browsing history, browsing environment (such as device, location, time, etc.), and application data (such as user performances on exercises). The goal of the research presented here is not to propose novel forms or applications of adaptation, but instead to extend widespread web standards with adaptation functionalities. Essential features of the proposed approach are its simplicity and both the upwards and downwards compatibility of the extension