An Evaluation of Combinations of Lossy Compression and ChangeDetection Approaches. (Technical Report ’15)

Today, time series of numerical data are ubiquitous, for instance in the Internet of Things. In such scenarios, it is often necessary to compress the data and to detect changes on it. More specifically, both methods are used in combination, i.e., data is lossily compressed and later decompressed, and then change detection takes place. There exists a broad variety of compression as well as of change-detection techniques. This calls for a systematic comparison of different combinations of compression and change-detection techniques, for different data sets, together with recommendations on how the values of the various (typically non-linear) parameters should be chosen. This article is such an evaluation. Its design is not trivial, necessitating a number of decisions. We work out the details and the rationale behind our design choices. Next to other results, our study shows that the choice of combinations of change detection and compression algorithm and their parameterization does affect result quality significantly. Our evaluation also indicates that results are highly contingent on the nature of the data.ers specify their privacy requirements as so-called discriminative pairs of secrets, and it perturbs data so that an adversary does not learn about the probability distribution of such pairs. However, deploying the framework on complex data such as time series requires application specific work. This includes a general definition of the representation of secrets in the data. Another issue is that the tradeoff between Pufferfish privacy and utility of the data is largely unexplored in quantitative terms. In this study, we quantify this tradeoff for smart meter data. Such data contains fine-grained time series of power-consumption data from private households. Disseminating such data in an uncontrolled way puts privacy at risk. We investigate how time series of energy consumption data must be transformed to facilitate specifying secrets that Pufferfish can use. We ensure the generality of our study by looking at different information-extraction approaches, such as re-identification and non-intrusive-appliance-load monitoring, in combination with a comprehensive set of secrets. Additionally, we provide quantitative utility results for a real-world application, the so-called local energy market

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Review and classification of trajectory summarisation algorithms: From compression to segmentation

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

A Deep Wavelet AutoEncoder Scheme for Image Compression

For many years and since its appearance, Digital Wavelet Transform DWT has been used with great success in a wide range of applications especially in image compression and signal de-noising. Combined with several and various approaches, this powerful mathematical tool has shown its strength to compress images with high compression ratio and good visual quality. This paper attempts to demonstrate that it is needless to follow the classical three stages process of compression: pixels transformation, quantization and binary coding when compressing images using the baseline method. Indeed, in this work, we propose a new scheme of image compression system based on an unsupervised convolutional neural network AutoEncoder (CAE) that will reconstruct the approximate sub-band issue from image decomposition by the wavelet transform DWT. In order To evaluate the model’s performance we use Kodak dataset containing a set of 24 images never compressed with a lossy algorithm technique and applied the approach on every one of them. We compared our achieved results with those obtained using standard compression method. We draw this comparison in terms of four performance parameters: Structural Similarity Index Metrix SSIM, Peak Signal to Noise Ratio PSNR, Mean Square Error MSE and Compression Ratio CR. The proposed scheme offers significate improvement in distortion metrics over the traditional image compression method when evaluated for perceptual quality moreover it produces better visual quality images with clearer details and textures which demonstrates its effectiveness and its robustness

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book