Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.

Verification of Service-Based Declarative Business Processes

Traditional business process specification approaches such as BPMN are procedural, as they require specifying exact and complete process flow. In contrast, a declarative process is specified by a set of constraints that mark the boundary of any solution to the process. In this chapter, the authors propose a bounded model-checking-based approach for the verification of declarative processes using satisfiability solving (SAT). The proposed approach does not require exponential space and is very efficient. It uses the highly expressive event calculus (EC) as the modeling formalism, with a sound and complete EC to SAT encoding process. The verification process can include both the functional and non-functional aspects. The authors have also proposed a filtering criterion to filter the clauses of interest from the large set of unsatisfiable clauses for complex processes. The authors have discussed the implementation details and performance evaluation results to justify the practicality of the proposed approach.

Minimum Database Determination and Preprocessing for Machine Learning

The exploitation of large databases implies the investment of expensive resources both in terms of the storage and processing time. The correct assessment of the data implies that pre-processing steps be taken before its analysis. The transformation of categorical data by adequately encoding every instance of categorical variables is needed. Encoding must be implemented that preserves the actual patterns while avoiding the introduction of non-existing ones. The authors discuss CESAMO, an algorithm which allows us to statistically identify the pattern preserving codes. The resulting database is more economical and may encompass mixed databases. Thus, they obtain an optimal transformed representation that is considerably more compact without impairing its informational content. For the equivalence of the original (FD) and reduced data set (RD), they apply an algorithm that relies on a multivariate regression algorithm (AA). Through the combined application of CESAMO and AA, the equivalent behavior of both FD and RD may be guaranteed with a high degree of statistical certainty.

Overlapping Singer Identification in Music Recordings

Singer identification (SID), which refers to the task of automatically identifying the singer(s) in a music recording, is of great help in handling the rapid proliferation of music data on the internet and digital media. Although a number of SID studies from acoustic features have been reported, most systems are designed to identify the singer in recordings of solo performances. Very little research has considered a more realistic case, which is to identify more than one singer in a music recording. The research presented in this chapter investigates the feasibility of identifying singers in music recordings that contain overlapping (simultaneous) singing voices (e.g., duet or trio singings). This problem is referred to as overlapping singer identification (OSID). Several approaches to OSID are discussed and evaluated in this chapter. In addition, a related issue on how to distinguish solo singings from overlapping singing recordings is also discussed.

Model-Based Development and Spatiotemporal Behavior of Cyber-Physical Systems

Many cyber-physical systems operate together with others and with humans in a joint physical space. Because of their operation in proximity to humans, they have to operate according to very high safety standards. This chapter presents a method for developing the control software of cyber-physical systems. The method is model-based and assists engineers with spatial and real-time property verification. In particular, the authors describe a toolchain consisting of the model-based development toolset Reactive Blocks, the spatial analyzer BeSpaceD in conjunction with the real-time model checkers UPPAAL and PRISM. The combination of these tools makes it possible to create models of the control software and, if necessary, simulators for the actual system behavior with Reactive Blocks. These models can then be checked for various correctness properties using the analysis tools. If all properties are fulfilled, Reactive Blocks transforms the models automatically into executable code.

Pattern-Based Cloud Migration

To migrate on-premises business systems to the cloud environment faces challenges: the complexity, diversity of the legacy systems, cloud, and cloud migration services. Consequently, the cloud migration faces two major problems. The first one is how to select cloud services for the legacy systems, and the second one is how to move the corresponding workload from legacy systems to cloud. This chapter presents a total cloud migration solution including cloud service selection and optimization, cloud migration pattern generation, and cloud migration pattern enforcement. It takes the pattern as the core, and unifies the cloud migration request, the cloud migration service pattern, and the cloud migration service composition. A cloud migration example of blockchain system shows that the proposed approach improves the cloud service selection, cloud migration service composition generation efficiency, migration process parallelization, and enables long transaction support by means of pattern reuse.

Context and End-User Privacy Policies in Web Service-Based Applications

Privacy protection plays a vital role in pervasive and web environments, where users contact applications and services that may require access to their sensitive data. The current legislation, such as the recent European General Data Protection Regulation, is putting more emphasis on user protection and on placing users in the center of privacy choices. SOAP (simple object access protocol)-based and RESTful services may require access to sensitive data for their proper functioning, but users should be able to express their preferences on what should and should not be accessed. In this chapter, the above issues are discussed and a solution is presented for reconciling user preferences expressed in privacy policies and the service data needs tailored to SOAP-based services. A use example is provided and the main open issues providing directions for future research are discussed.

Multi-Level Web Service Clustering to Bootstrap the Web Service Discovery and Selection

Existing technologies for web services have been extended to give the value-added customized services to users through the service composition. Service composition consists of four major stages: planning, discovery, selection, and execution. However, with the proliferation of web services, service discovery and selection are becoming challenging and time-consuming tasks. Organizing services into similar clusters is a very efficient approach. Existing clustering approaches have problems that include discovering semantic characteristics, loss of semantic information, and a shortage of high-quality ontologies. Thus, the authors proposed hybrid term similarity-based clustering approach in their previous work. Further, the current clustering approaches do not consider the sub-clusters within a cluster. In this chapter, the authors propose a multi-level clustering approach to prune the search space further in discovery process. Empirical study of the prototyping system has proved the effectiveness of the proposed multi-level clustering approach.

An Integrated Framework for Semantic Service Composition Using Answer Set Programming (SSC-ASP)

Notwithstanding the advancement of service computing in recent years, service composition is still a main issue in this field. In this chapter, the authors present an integrated framework for semantic service composition using answer set programming. Unlike the AI planning approaches of top-down workflow with nested composition and combining composition procedure into service discovery, the proposed framework integrates a designed service workflow with automatic nested composition. In addition, the planning is based on service signature while validating through service contract. Moreover, a unified implementation of service discovery, selection, composition, and validation is achieved by answer set programming. Finally, the performance of proposed framework is demonstrated by a travel booking example on QWSDataset.