Search results for " artificial intelligence"

Improving Lossless Image Compression with Contextual Memory

2019

With the increased use of image acquisition devices, including cameras and medical imaging instruments, the amount of information ready for long term storage is also growing. In this paper we give a detailed description of the state-of-the-art lossless compression software PAQ8PX applied to grayscale image compression. We propose a new online learning algorithm for predicting the probability of bits from a stream. We then proceed to integrate the algorithm into PAQ8PX&rsquo

A Pragmatic Characterization of Concept Algebra

2017

Taking into account the framework of denotational mathematics as seen by Yingxu Wang, in this paper the author wishes to implement a possible further pragmatic (context-depend) dimension into the algebraic structure of concept algebra. One of the main problems of software science is that regarding context-depend question of a programming language. Indeed, attention has been paid above all to syntactic and semantic dimensions of a programming language, neglecting the pragmatic one concerning context. The author has tried to face this question providing a first denotational mathematics structure taking into account a possible pragmatic dimension.

Fuzzy Logic Based Security Trust Evaluation for IoT Environments

2019

In the new technological era called the Internet of Things (IoT), people, machines and objects communicate with each other via the Internet by exchanging information. In this context, trust plays an important role and is considered as a key factor in the success of the IoT services expansion. IoT services and applications use in some cases data concerning the privacy of their users. Consequently, users should trust the entities exchanging their personal information. In this paper, we present a framework that evaluates the security trust level of IoT nodes based on a Fuzzy Logic model using different input parameters such as Device Physical Security, Device Security Level and Device Ownershi…

A Wavelet approach to extract main features from indirect immunofluorescence images

2019

A number of previous studies have shown that IIF image analysis requires complex and sometimes heterogeneous and diversified methods. Robust solutions can be proposed but they need to orchestrate several methods from low-level analysis up to more complex neural networks or SVM for data classification. The contribution intends to highlight the versatility of Wavelet Transform (WT) and their use in various levels of analysis for the classification of IIF images in order to develop a system capable of performing: image enhancement, ROI segmentation and object classification. Therefore, WT was adopted in the de-noise section, segmentation and classification. This analysis allows frequencies cha…

Towards a Hierarchical Multitask Classification Framework for Cultural Heritage

2018

Digital technologies such as 3D imaging, data analytics and computer vision opened the door to a large set of applications in cultural heritage. Digital acquisition of a cultural assets takes nowadays a couple of seconds thanks to the achievements in 2D and 3D acquisition technologies. However, enriching these cultural assets with labels and relevant metadata is still not fully automatized especially due to their nature and specificities. With the recent publication of several cultural heritage datasets, many researchers are tackling the challenge of effectively classifying and annotating digital heritage. The challenges that are often addressed are related to visual recognition and image c…

Querying and reasoning over large scale building data sets

2016

International audience; The architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is avai…

Executable Data Quality Models

2017

The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in c…

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.

2020

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms,…

Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

2014

Least-squares community extraction in feature-rich networks using similarity data

2021

We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may …