Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

Multi-task learning for intelligent data processing in granular computing context

Classification is a popular task in many application areas, such as decision making, rating, sentiment analysis and pattern recognition. In the recent years, due to the vast and rapid increase in the size of data, classification has been mainly undertaken in the way of supervised machine learning. In this context, a classification task involves data labelling, feature extraction,feature selection and learning of classifiers. In traditional machine learning, data is usually single-labelled by experts, i.e., each instance is only assigned one class label, since experts assume that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in real applications. For example, in the context of emotion detection, there could be more than one emotion identified from the same person. On the other hand, feature selection has typically been done by evaluating feature subsets in terms of their relevance to all the classes. However, it is possible that a feature is only relevant to one class, but is irrelevant to all the other classes. Based on the above argumentation on data labelling and feature selection, we propose in this paper a framework of multi-task learning. In particular, we consider traditional machine learning to be single task learning, and argue the necessity to turn it into multi-task learning to allow an instance to belong to more than one class (i.e., multi-task classification) and to achieve class specific feature selection (i.e.,multi-task feature selection). Moreover, we report two experimental studies in terms of fuzzy multi-task classification and rule learning based multi-task feature selection. The results show empirically that it is necessary to undertake multi-task learning for both classification and feature selection

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition

Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0-9) is viewed as a class and the machine learning task is essentially to train a classifier that can effectively discriminate the ten classes. In practice, it is very usual that the performance of a single classifier trained by using a standard learning algorithm is varied on different data sets, which indicates that the same learning algorithm may train strong classifiers on some data sets but weak classifiers may be trained on other data sets. It is also possible that the same classifier shows different performance on different test sets, especially when considering the case that image instances can be highly diverse due to the different handwriting styles of different people on the same digits. In order to address the above issue, development of ensemble learning approaches have been very necessary to improve the overall performance and make the performance more stable on different data sets. In this paper, we propose a framework that involves CNN based feature extraction from the MINST data set and algebraic fusion of multiple classifiers trained on different feature sets, which are prepared through feature selection applied to the original feature set extracted using CNN. The experimental results show that the classifiers fusion can achieve the classification accuracy of ≥ 98%

Cognitive computing in the travel and tourism industry

Cognitive computingemergedfromamixtureofcognitivescience and computer science. Although cognitive computing is seen as a challenge for com panies, it is an opportunity that raises the bar for the services provided, allowing companies that are committed to investing in innovation, and in this particular case in the travel and tourism sector, being able to stand out from the compe tition by adopting new technologies that make it possible to offer revolutionary and value-added experiences. Whether optimizing processes or developing new services, this technology is the key to innovation and competitiveness in the travel and tourism sector. With the user’s history and previous search data, cognitive systems, even before the user realizes it, present specific options according to their profile\preferences. In this sense, the system has the ability to automatically restrict the entire travel package. The main objective of this work is to explore the area of Cognitive computing in the context of Travel and Tourism Industry.info:eu-repo/semantics/publishedVersio