PERSONAF: framework for personalised ontological reasoning in pervasive computing

Pervasive computing creates possibilities for presenting highly personalised information about the people, places and things in a building. One of the challenges for such personalisation is the creation of the system that can support ontological reasoning for several key tasks: reasoning about location; personalisation of information about location at the right level of detail; and personalisation to match each person’s conceptions of the building based on their own use of it and their relationship to other people in the building. From pragmatic perspectives, it should be inexpensive to create the ontology for each new building. It is also critical that users should be able to understand and control pervasive applications. We created the PERSONAF (personalised pervasive scrutable ontological framework) to address these challenges. PERSONAF is a new abstract framework for pervasive ontological reasoning. We report its evaluation at three levels. First, we assessed the power of the ontology for reasoning about noisy and uncertain location information, showing that PERSONAF can improve location modelling. Notably, the best ontological reasoner varies across users. Second, we demonstrate the use of the PERSONAF framework in Adaptive Locator, an application built upon it, using our low cost mechanisms for non-generic layers of the ontology. Finally, we report a user study, which evaluated the PERSONAF approach as seen by users in the Adaptive Locator. We assessed both the personalisation performance and the understandability of explanations of the system reasoning. Together, these three evaluations show that the PERSONAF approach supports building of low cost ontologies, that can achieve flexible ontological reasoning about smart buildings and the people in them, and that this can be used to build applications which give personalised information that can provide understandable explanations of the reasoning underlying the personalisation.     

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Improved feature selection algorithm with fuzzy-rough sets on compact computational domain

The aim of this paper is to provide an efficient input feature selection algorithm for modeling of systems based on modified definition of fuzzy-rough sets. Some of the critical issues concerning the complexity and convergence of the feature selection algorithm are discussed in detail. Based on some natural properties of fuzzy t-norm and t-conorm operators, the concept of fuzzy-rough sets on compact computational domain is put forward, which is then utilized to construct improved Fuzzy-Rough Feature Selection algorithm. Various mathematical properties of this new definition of fuzzy-rough sets are discussed from pattern classification viewpoint. Speedup factor as high as 622 has been achieved with proposed algorithm compared to recently proposed FRSAR, with improved model performance on selected set of features.     

DEX: self-healing expanders

We present a fully-distributed self-healing algorithm dex that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders—whose expansion properties hold deterministically—that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees on the network expansion which rapidly degrade in a dynamic setting; in particular, the expansion properties can degrade even more rapidly under adversarial insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only \(O(\log n)\) rounds and \(O(\log n)\) messages are needed with high probability (n is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table on top of dex  with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have guaranteed properties (constant bounded degree and expansion) despite dynamic changes.     

Development of firefly algorithm via chaotic sequence and population diversity to enhance the image contrast

Nature-inspired algorithms have been applied in the optimization field including digital image processing like image enhancement or segmentation. Firefly algorithm (FA) is one of the most powerful of them. In this paper two different implementation of FA has been taken into consideration. One of them is FA via lévy flight where step length of lévy flight has been taken from chaotic sequence. Chaotic sequence shows ergodicity property which helps in better searching. But in the second implementation chaotic sequence replaces lévy flight to enhance the capability of FA. Population of individuals has been created in every generation using the information of population diversity. As an affect FA does not converges prematurely. These two modified FA algorithms have been applied to optimize parameters of parameterized contrast stretching function. Entropy, contrast and energy of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it has been shown that FA with chaotic sequence and population diversity information outperforms the Particle swarm optimization and FA via lévy flight.     

Factors affecting the adoption of cloud services in enterprises

Cloud computing services offer enterprise clients many advantages such as reduced costs, easy maintenance and the easy re-provisioning of resources, thus contributing to increased profits. However, little is known about the adoption behavior of such services among enterprises. This study applies the technology–organization–environment framework to investigate determinants of cloud computing service adoption behavior. Data collected from 102 valid enterprises in Taiwan provide strong support for the model. Results indicate that technological (i.e., relative advantage, observability and security), organizational (i.e., financial costs and satisfaction with existing IS) and environmental (i.e., competition intensity) factors were positively related to intention to adopt cloud computing services, accounting for 52 % of variance. Implications and limitations are discussed.     

Context inclusive function evaluation: a case study with EM-based multi-scale multi-granular image classification

Many statistical queries such as maximum likelihood estimation involve finding the best candidate model given a set of candidate models and a quality estimation function. This problem is common in important applications like land-use classification at multiple spatial resolutions from remote sensing raster data. Such a problem is computationally challenging due to the significant computation cost to evaluate the quality estimation function for each candidate model. For example, a recently proposed method of multi-scale, multi-granular classification has high computational overhead of function evaluation for various candidate models independently before comparison. In contrast, we propose an upper bound based context-inclusive approach that reduces computational overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. We also prove that an upper bound exists for each candidate model and the proposed algorithm is correct. Experimental results using land-use classification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly.     

A test-first view of usability

The foundations of usability evaluation were being established in the early 1990s. In this context, “Usability – Context, Definition, Design and Evaluation” built upon Brian Shackel’s earlier influential work – work that helped define the notion of usability. In this paper, he established key dimensions of usability as well as approaches to integrating the testing of these dimensions, within the whole process of setting requirements. Essentially he argued for usability design as part of the system design process.This commentary describes the context of Professor Shackel’s paper and reviews the influential ideas that appear in much subsequent work.     

Meta-Heuristic Algorithms-Tuned Elman vs. Jordan Recurrent Neural Networks for Modeling of Electron Beam Welding Process

Neural Processing Letters - A boost in the preference of high energy beam, such as electron beam, laser beam etc. has led to the requirement of its automation through accurate input–output...     

Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement

Image enhancement can be formulated as an optimization problem where one parameterized transformation function is used for enhancement purpose. The proper enhancement significantly depends on two factors- fine tuning of the parameters of the corresponding parameterized transformation function and other one is the selection of a proper objective function. In this study a parameterized variant of histogram equalization (HE) has been used for enhancement purpose and to tune the parameters of that variant a modified cuckoo search (CS) with new global and local search strategies is employed. This paper also concentrates on the selection of a proper objective function to preserve the original brightness of the image. A new objective function has been developed by combining fractal dimension (FD) and quality index based on local variance (QILV). Visual analysis and experimental results prove that modified CS with search strategies outperforms the traditional and some other existing modified CS algorithms. Considering the image’s brightness preserving capability, the proposed objective function significantly outperforms other existing objective functions.