Web-based Application Programming Interface (API) has become an important tool for modern software development. Many enterprises have developed various types of web APIs to support their business services, such as Google Map APIs, Twitter APIs, and eBay APIs. Due to the huge number of web APIs available in public domain, unfortunately, choosing relevant and low-risk web APIs has become an important problem for developers. This research is aimed at enhancing the recom- mendation engine for web APIs from several aspects. First, a new scanning technique is developed to detect the usage of web APIs in source codes. Using our scanning technique, we scanned over 1.7 million Open Source projects to capture the API usage patterns. Second, we integrated three machine learning models to predict compliance risks from web APIs based on their terms of services or other legal documents. Third, utilizing the knowledge learned from scanning results and compliance risks, we built a new recommendation engine for web APIs. We conducted an experimental study to evaluate our Web API recommendation engine and demonstrate its effectiveness. Some other modules, such as finding similar web APIs and searching function-related web APIs, have also been discussed. / Graduate

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With the emergence of peer-to-peer video-on-demand systems, new avenues for keeping track of and subsequently meeting user needs and desires have arisen. Based on the idea of contextual priming, we introduce a new frame-work, SUE, that takes advantage of the intimate level of user profiling afforded by the internet as well as the linear and segmented nature of p2p technology to determine a user's exact on-screen experience at any given time interval. This allows us to more accurately determine the type of information a user is likely to be more receptive to. Our design differs from other existing systems in two ways: (a) the level of granularity it can support, accommodating factors from both the user's on-screen and physical environment in making its recommendations and (b) in addressing some of the shortcomings seen in current applications, such as those imposed by coarse user profiling and faulty associations. In order to examine the viability of our framework, we provide a high level design specifying its incorporation into an existing p2p video system, the BitVampire project.

With the emergence of peer-to-peer video-on-demand systems, new avenues for keeping track of and subsequently meeting user needs and desires have arisen. Based on the idea of contextual priming, we introduce a new frame-work, SUE, that takes advantage of the intimate level of user profiling afforded by the internet as well as the linear and segmented nature of p2p technology to determine a user's exact on-screen experience at any given time interval. This allows us to more accurately determine the type of information a user is likely to be more receptive to. Our design differs from other existing systems in two ways: (a) the level of granularity it can support, accommodating factors from both the user's on-screen and physical environment in making its recommendations and (b) in addressing some of the shortcomings seen in current applications, such as those imposed by coarse user profiling and faulty associations. In order to examine the viability of our framework, we provide a high level design specifying its incorporation into an existing p2p video system, the BitVampire project.

No description available.

With the emergence of peer-to-peer video-on-demand systems, new avenues for keeping track of and subsequently meeting user needs and desires have arisen. Based on the idea of contextual priming, we introduce a new frame-work, SUE, that takes advantage of the intimate level of user profiling afforded by the internet as well as the linear and segmented nature of p2p technology to determine a user's exact on-screen experience at any given time interval. This allows us to more accurately determine the type of information a user is likely to be more receptive to. Our design differs from other existing systems in two ways: (a) the level of granularity it can support, accommodating factors from both the user's on-screen and physical environment in making its recommendations and (b) in addressing some of the shortcomings seen in current applications, such as those imposed by coarse user profiling and faulty associations. In order to examine the viability of our framework, we provide a high level design specifying its incorporation into an existing p2p video system, the BitVampire project. / Science, Faculty of / Computer Science, Department of / Graduate

With the extension of the World Wide Web, more than 7 million new pages being exploited each day, the problem of information overload due to a large number of coping, spreading and sharing causes the decreasing information quantity, diverse format of information and low quality of information. More researchers research what methods including search and recommendation can help users gather the critical topics. Whatever methods use historical purchasing or browsing data to find the proper information usually, can not considering all attributes affecting decision results. Therefore, the research uses Analytic Hierarchy Process belonging Multiple Critical Decision Theory to develop recommendation system. Providing a single, easy understand model, using hierarchic structuring reflecting the nature tendency of the mind, considering all attributes affecting decision results, Analytic Hierarchy Process takes into consideration the relative priorities and select the best alternative. Furthermore, it can show the subjective consciousness of the user in a structure way and assist designer to determine a more rational and conformable judgment. Based on Analytic Hierarchy Process, the commerce recommendation system expects to help user to find more satisfactory merchandise. It is found that the recommendation system using Analytic Hierarchy Process finds the accurate products for user and gets the higher satisfaction. However, the operation satisfaction is not higher that rank-based but still in available and satisfaction scope.

The simplicity and flexibility of tagging allows users to collaboratively create large, loosely structured repositories of Web resources. One of its main drawbacks is the need for manual formulation of tags for each posted resource. This task can be eased by a tag recommendation system, the objective of which is to propose a set of tags for a given resource, user pair. Tag recommendation is an interesting and well-defined practical problem. Its main features are constant interaction with users and availability of large amounts of tagged data. Given the opportunities (e.g., rich user feedback) and limitations (e.g., real-time response) of the tag recommendation setting, we defined six requirements for a practically useful tag recommendation system. We present a conceptual design and system architecture of a hybrid tag recommendation system, which meets all these requirements. The system utilizes the strengths of various tag sources (e.g., resource content and user profiles) and the relations between concepts captured in tag co-occurrence graphs mined from collaborative actions of users. The architecture of the proposed system is based on a text indexing engine, which allows the system to deal with large datasets in real time, while constantly adapting its models to newly added posts. The effectiveness and efficiency of the system was evaluated for six datasets representing a broad range of collaborative tagging systems. The experiments confirmed the high quality of results and practical usability of the system. In a comparative study the system outperformed a state-of-the-art algorithm based on tensor factorization for the most representative datasets applicable to both methods. The experiments on the characteristics of tagging data and the performance of the system allowed us to find answers to important research questions adapted from the general area of recommender systems. We confirmed the importance of infrequently used tags in the recommendation process and proposed solutions to overcome the cold start problem in tag recommendation. We demonstrated that a parameter tuning approach makes a hybrid tag recommendation system adaptable to various datasets. We also revealed the importance of the utilization of a feedback loop in the tag recommendation process.

No description available.

PURPOSE: Although Intra-oral radiographs are foundational for diagnosis and planning treatment in dentistry, the resulting 2-dimensional image varies in interpretation requiring judgement. Cone Beam Computed Tomography provides a more detailed 3-dimensional image that may affect treatment recommendations. This study aimed to determine the basis for CBCT recommendations and its effect on diagnosis and treatment planning. METHODS: The study involved a sample of 45 cases that presented for endodontic treatment, 30 with a CBCT scan on record and 15 without. For phase I, all 45 cases were reviewed by 3 examiners without access to the CBCT scans. Four months later for phase II, the 3 examiners re-analyzed the 30 cases, this time with the associated CBCT. Intra and inter-examiner agreements were recorded and analyzed. Also, the recommendations for CBCT were compared to the AAE/AAOMR Joint Statement. RESULTS: Inter-examiners agreement in phases I and II were 65% and 72% respectively. For endodontic diagnoses, there was 19% change in the pulpal diagnosis category when CBCT was added, while there was 30% change in the apical category. The selections changed in 55% of the cases when determining etiology, and in 49% of the cases when making recommendations. CBCT was recommended 78.8% of the time when the case had a CBCT on record vs. 33% of the time in cases without. CONCLUSION: CBCT has a significant effect in determining endodontic pathology’s etiology and recommending treatment. Further, CBCT is not over prescribed in the endodontic department and the faculty adhere largely to the joint AAE/AAOMR recommendations. / 2021-06-23T00:00:00Z