A fuzzy multiple attribute decision making tool for HVAC&R systems selection with considering the future probabilistic climate changes and electricity decarbonisation plans in the UK

Buildings account for 40% of total energy consumption in the UK and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand and the ascending trend in utilising HVAC&R systems together with the global need to impose energy-efficiency measures underline the importance of selecting the most appropriate HVAC&R system during the design process. This paper reviewed and classified a broad range of principal multiple attribute decision making methods. Among them, the fuzzy multiple attribute decision making approach was adopted to develop a decision making tool for HVAC&R systems selection. This was mainly due to the ability of this method to deal with the uncertainties and imprecisions of the linguistic terms involved in the decision making process. In order to make a decision on HVAC&R systems selection, 58 alternative systems, including both primary and secondary parts, were examined. The scope of this study enabled the consideration of all 18 climate regions in the UK and included the effects of climate change. In addition, the Government’s electricity decarbonisation plans were integrated within the developed decision making model for HVAC&R systems selection in office buildings in the UK. Finally, the model was transferred into a computational tool with a user-friendly interface

Relaxed Functional Dependencies - A Survey of Approaches

Recently, there has been a renovated interest in functional dependencies due to the possibility of employing them in several advanced database operations, such as data cleaning, query relaxation, record matching, and so forth. In particular, the constraints defined for canonical functional dependencies have been relaxed to capture inconsistencies in real data, patterns of semantically related data, or semantic relationships in complex data types. In this paper, we have surveyed 35 of such functional dependencies, providing a classification criteria, motivating examples, and a systematic analysis of them

Software defect prediction using maximal information coefficient and fast correlation-based filter feature selection

Software quality ensures that applications that are developed are failure free. Some modern systems are intricate, due to the complexity of their information processes. Software fault prediction is an important quality assurance activity, since it is a mechanism that correctly predicts the defect proneness of modules and classifies modules that saves resources, time and developers’ efforts. In this study, a model that selects relevant features that can be used in defect prediction was proposed. The literature was reviewed and it revealed that process metrics are better predictors of defects in version systems and are based on historic source code over time. These metrics are extracted from the source-code module and include, for example, the number of additions and deletions from the source code, the number of distinct committers and the number of modified lines. In this research, defect prediction was conducted using open source software (OSS) of software product line(s) (SPL), hence process metrics were chosen. Data sets that are used in defect prediction may contain non-significant and redundant attributes that may affect the accuracy of machine-learning algorithms. In order to improve the prediction accuracy of classification models, features that are significant in the defect prediction process are utilised. In machine learning, feature selection techniques are applied in the identification of the relevant data. Feature selection is a pre-processing step that helps to reduce the dimensionality of data in machine learning. Feature selection techniques include information theoretic methods that are based on the entropy concept. This study experimented the efficiency of the feature selection techniques. It was realised that software defect prediction using significant attributes improves the prediction accuracy. A novel MICFastCR model, which is based on the Maximal Information Coefficient (MIC) was developed to select significant attributes and Fast Correlation Based Filter (FCBF) to eliminate redundant attributes. Machine learning algorithms were then run to predict software defects. The MICFastCR achieved the highest prediction accuracy as reported by various performance measures.School of ComputingPh. D. (Computer Science

Improved Designs for Application Virtualization

We propose solutions for application virtualization to mitigate the performance loss in streaming and browser-based applications. For the application streaming, we propose a solution which keeps operating system components and application software at the server and streams them to the client side for execution. This architecture minimizes the components managed at the clients and improves the platform-level incompatibility. The runtime performance of application streaming is significantly reduced when the required code is not properly available on the client side. To mitigate this issue and boost the runtime performance, we propose prefetching, i.e., speculatively delivering code blocks to the clients in advance. The probability model on which our prefetch method is based may be very large. To manage such a probability model and the associated hardware resources, we perform an information gain analysis. We draw two lower bounds of the information gain brought by an attribute set required to achieve a prefetch hit rate. We organize the probability model as a look-up table: LUT). Similar to the memory hierarchy which is widely used in the computing field, we separate the single LUT into two-level, hierarchical LUTs. To separate the entries without sorting all entries, we propose an entropy-based fast LUT separation algorithm which utilizes the entropy as an indicator. Since the domain of the attribute can be much larger than the addressable space of a virtual memory system, we need an efficient way to allocate each LUT\u27s entry in a limited memory address space. Instead of using expensive CAM, we use a hash function to convert the attribute values into addresses. We propose an improved version of the Pearson hashing to reduce the collision rate with little extra complexity. Long interactive delays due to network delays are a significant drawback for the browser-based application virtualization. To address this, we propose a distributed infrastructure arrangement for browser-based application virtualization which reduces the average communication distance among servers and clients. We investigate a hand-off protocol to deal with the user mobility in the browser-based application virtualization. Analyses and simulations for information-based prefetching and for mobile applications are provided to quantify the benefits of the proposed solutions

A First Study on What MDL Can Do for FCA

International audienceFormal Concept Analysis can be considered as a classification engine able to build classes of objects with a description or concepts and to organize these concepts within a concept lattice. The concept lattice can be navigated for selecting significant concepts. Then the problem of finding significant concepts among the potential exponential number of concepts arises. Some measures exist that can be used for focusing on interesting concepts such as support, stability, and other. MDL (mini-mum description length) is also a good candidate that was rarely used in FCA by now for such objective. In this paper we argue that MDL can give FCA practitioners a good measure for selecting significant and representative concepts