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Whilst new patents and announcements advertise the technical availability of foldable displays, which are capable to be folded to some extent, there is still a lack of fundamental and applied understanding of how to model, to design, and to prototype graphical user interfaces for these devices before actually implementing them. Without waiting for their off-the-shelf availability and without being tied to any physical foldable mechanism, Flecto defines a model, an associated notation, and a supporting software for prototyping graphical user interfaces running on foldable displays, such as foldable smartphone or assemblies of foldable surfaces. For this purpose, we use an extended notation of the Yoshizawa-Randlett diagramming system, used to describe the folds of origami models, to characterize a foldable display and define possible interactive actions based on its folding operations. A guiding method for rapidly prototyping foldable user interfaces is devised and supported by Flecto, a design environment where foldable user interfaces are simulated in 3D environment instead of in physical reality. We report on a case study to demonstrate Flecto in action and we gather the feedback from users on Flecto, using Microsoft Product Reaction Cards. CCS Concepts: • Human-centered computing → Displays and imagers; Interactive systems and tools; Graphical user interfaces; Virtual reality; Ubiquitous and mobile computing design and evaluation methods; • Software and its engineering → Integrated and visual development environments; Rapid application development.

Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy.

After we have showing that lower bound is not the only important parameter t o balance subproblems between queues of best-first B&B distributed algorithms, we introduced another notion of priority between subproblems that takes into account not only their lower bound but also their capacity to generate other subproblems. We developed three load balancing strategies using this new notion of priority, with a B&B solution of the Vertex Cover Problem (VCP). The implementation of the VCP was carried out in a network of het,erogeneous Unix workstations used as a single parallel computer through a Parallel Virtual Machine (PVM) softwa,re system. To show the advantages of the new notion of priority, we solved the VCP for graphs of up to 130 nodes with average degree of 50%. Also, we were able to achieve a remarkable load dist,ribution and a good restriction of conimunica.t~ioii cost,s over all the different machines in the system.

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