Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.

Evaluating Spirulina as a protein source in Nile Tilapia (Oreochromis niloticus) grow-out diets

Aquaculture growth has intensified the need for a diversification of nutritionally appropriate aquafeed ingredients. The purpose of this study was to evaluate Spirulina, a blue-green microalgae, and soybean meal as the sole protein sources in grow-out Tilapia diets. We constructed 3 experimental diets with soybean meal and 0,15, 30, and 45% Spirulina (SBM, SP15, SP30, and SP45 respectively) as their main protein sources. We compared these diets to a commercial Tilapia diet (CC). Additionally, to evaluate the benefit of fishmeal inclusion, fishmeal was added (2 and 10%) to the most successful Spirulina containing diet (FM2, FM10). We evaluated these experimental diets based on their physical properties, palatability, growth potential, waste production, and overall cost. No significant differences in growth performance were found between any of the diets. Total ammonia nitrogen (TAN) and total phosphorus (TP) levels in each tank were significantly affected by diet (p<0.05). CC had significantly higher TP than the experimental diets and SP15 had significantly higher TAN than the other diets. Only CC was found to be significantly more palatable than the experimental diets, and Spirulina inclusion was inversely correlated to pellet stability. Lastly, SP15 was the most profitable experimental diet. We recommend eliminating fishmeal from grow-out Tilapia diets in favour of soybean meal and Spirulina. Spirulina should, however, be limited to 15% to avoid the negative effects it has on stability and profitability, and its possible effect on feed intake.