Abstract
In this paper a Cooperation Mechanism for Distributed Scheduling based on Bees based Computing is proposed. Where multiple self-interested agents can reach agreement over the exchange of operations on cooperative resources. Agents must collaborate to improve their local solutions and the global schedule. The proposed cooperation mechanism is able to analyze the scheduling plan generated by the Resource Agents and refine it by idle times reducing taking advantage from cooperative and the self-organized behavior of Artificial Bee Colony technique. The computational study allows concluding about statistical evidence that the cooperation mechanism influences significantly the overall system performance.

Abstract
From long to short term planning, decision processes inherent to operating theater organization are often subject of empiricism, leading to far from optimal results. Waiting lists for surgery have always been a societal problem, which governments have been fighting with different management and operational stimulus plans. The current hospital information systems available in Portuguese public hospitals, lack a decision support system component that could help achieve better planning solutions. Thus, an intelligent decision support system has been developed, allowing the centralization and standardization of planning processes, improving the efficiency of the operating theater and tackling the waiting lists for surgery fragile situation. The intelligence of the system derives from data mining and optimization techniques, which enhance surgery duration predictions and operating rooms surgery schedules. Experimental results show significant gains, reducing overtime, undertime, and better resource utilization. Note to Practitioners-The Operating Theater (OT) is often considered hospitals' biggest budget consumer and revenue center in a hospital. This paper was motivated by a project that aims to reduce expenses and surgery waiting lists in Portuguese public hospitals, by developing an Intelligent Decision Support System (DSS) to support surgery scheduling. Prior to this research, decision makers (Surgeons, Department managers, Operating theatre managers) used their experience to make allocation, scheduling and estimation decisions. Since many of these decisions are made without analyzing past results, mistakes occur frequently, affecting the OT performance. With the help of business intelligence, data mining and optimization algorithms, surgeons' estimations can be more precise and the operating room schedule can be optimized. Preliminary experiments on the usage of DSS reveal a remarkable increase of the efficiency of the whole OT. In future research, we will extend the DSS and the techniques used to address the tactical master surgery scheduling problem, which aims to perform a better allocation of the different specialties to the operating rooms along the week. In addition, upstream and downstream resources shall be considered in the optimization module, as well as a simulation component to better evaluate generated solutions.

Abstract
Graphical user interfaces (GUIs) have become popular as they appear in everydays' software. GUIs have become an ideal way of interacting with computer programs, making the software friendlier to its users. GUIs have grown, and so has the usage of UI Patterns featured in GUIs. UI Patterns are recurring solutions to solve common GUI design problems. We developed the notion of UI Test Patterns that, are able to test different implementations of UI Patterns. Therefore, we created a new methodology called Pattern-Based GUI Testing (PBGT) that aims at systematizing and automating the GUI testing process. PBGT samples the input space using UI Test Patterns, which provide a reusable and configurable test strategy, in order to test a GUI that was implemented using a set of UI Patterns. In this paper we present three UI Test Patterns: Login, Master/Detail and Sort.

Abstract
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Abstract
Bayesian network structures are usually built using only the data and starting from an empty network or from a naive Bayes structure. Very often, in some domains, like medicine, a prior structure is already known based on expert knowledge. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers, while maintaining most of the interpretability of the original network.

Abstract
Taxi services play a central role in the mobility dynamics of major urban areas. Advanced communication devices such as GPS (Global Positioning System) and GSM (Global System for Mobile Communications) made it possible to monitor the drivers' activities in real-time. This paper presents an online learning approach to predict profitability in taxi stands. This approach consists of classifying each stand based according to the type of services that are being requested (for instance, short and long trips). This classification is achieved by maintaining a time-evolving histogram to approximate local probability density functions (p.d.f.) in service revenues. The future values of this histogram are estimated using time series analysis methods assuming that a non-homogeneous Poisson process is in place. Finally, the method's outputs were combined using a voting ensemble scheme based on a sliding window of historical data. Experimental tests were conducted using online data transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide an effective insight on the characterization of taxi stand profitability.