Learning Fuzzy Set Representations of Partial Shapes on Dual Embedding Spaces

Modeling relations between components of 3D objects is essential for many geometry editing tasks. Existing techniques commonly rely on labeled components, which requires substantial annotation effort and limits components to a dictionary of predefined semantic parts. We propose a novel framework based on neural networks that analyzes an uncurated collection of 3D models from the same category and learns two important types of semantic relations among full and partial shapes: complementarity and interchangeability. The former helps to identify which two partial shapes make a complete plausible object, and the latter indicates that interchanging two partial shapes from different objects preserves the object plausibility. Our key idea is to jointly encode both relations by embedding partial shapes as fuzzy sets in dual embedding spaces. We model these two relations as fuzzy set operations performed across the dual embedding spaces, and within each space, respectively. We demonstrate the utility of our method for various retrieval tasks that are commonly needed in geometric modeling interfaces.     

The DiamondCandy LRnLA algorithm: raising efficiency of the 3D cross-stencil schemes

The Journal of Supercomputing - The parallel efficiency is raised by increasing the locality of calculation. With the locally recursive non-locally asynchronous algorithms method, we have...     

Understanding governance,risk and compliance information systems (GRC IS): The experts view

Although Governance, Risk and Compliance (GRC) is an emerging field of study within the information systems (IS) academic community, the concept behind the acronym has to still be demystified and further investigated. The study investigates GRC systems in depth by (a) reviewing the literature on existing GRC studies, and (b) presenting a field study on views about GRC application by professional experts. The aim of this exploratory study is to understand the aspects and the nature of the GRC system following an enterprise systems approach. The result of this study is a framework of particular GRC characteristics that need to be taken into consideration when these systems are put in place. This framework includes specific areas such as: goals and objectives, purpose of the system, key stakeholders, methodology and requirements prior to implementation, critical success factors and problems/barriers. Further discussion about the issues, the concerns and the diverse views on GRC would assist in developing an agenda for the future research on the GRC field.     

Continuous physical activity recognition for intelligent labour monitoring

Multimedia Tools and Applications - The paper addresses the problem of human activity recognition based on the data from wearable sensors. Human activity recognition depends on a wide context of...     

Sparse CCA using a Lasso with positivity constraints

Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. A sparse version of CCA is proposed that reduces the chance of including unimportant variables in the canonical variates and thus improves their interpretation. A version of the Lasso algorithm incorporating positivity constraints is implemented in tandem with alternating least squares (ALS), to obtain sparse canonical variates. The proposed method is demonstrated on simulation studies and a data set from market basket analysis.     

An empirical investigation of Technology Readiness among medical staff based in Greek hospitals

Technology readiness (TR) represents an individual’s mental readiness to accept new technologies. Although the TR scale has been used in many studies, its application in the healthcare context is limited. This paper focuses on identifying the TR profiles of medical staff and to model preference TR variations with respect to computer use, computer knowledge and computer feature demands. The study reports results from a nationwide study conducted in Greece, during a three-year period, which sampled responses from 604 physicians and nurses working in 14 Greek hospitals. Exploratory Structural Equation Modelling analysis is used in order to confirm the structure of the Technology Readiness Index. The results confirm the five groups of the TR taxonomy. Statistical differences were found between classes in information and communication technology (ICT) knowledge, ICT feature demands, hours of use per week as well as ICT use performance, but not in the general use of ICT. The results facilitate comprehension of the factors, which influence the use of ICT by medical staff and, in addition, they convey important policy and managerial implications. In conclusion, medical staff should be treated according to its TR taxonomy classes in order to expedite the acceptance and use of an ICT system.     

Secure electronic bills of lading: blind counts and digital signatures

Electronic documents used in the framework of the goods delivery industry—i.e. electronic bills of lading (e-BOLs)—are the enablers of any payment, and therefore exposed to frauds. As of today, e-BOLs are handled by special private companies, which provide paperless trading services through their trade chains. This paper contributes a zero-knowledge open solution to the problem of designing secure electronic bills of lading, in the framework of a shipper-carrier-buyer transmission model. The suggested solution is a cryptographic protocol based on digital signatures and blind merchandise counts—that is, counts that do not reveal any information about actually counted quantities. The model is designed to mitigate a number of security threats and assumes the existence of both a trusted third party and a bank in charge of payment procedures. The paper discusses the drawbacks of the existing proprietary solutions and shows how the suggested open protocol addresses them.     

The contribution of proteomics towards deciphering the enigma of Coxiella burnetii

Coxiella burnetii, the causative agent of Q fever, is an intracellular bacterium and a potential weapon for bioterrorism. The widespread throughout the world, zoonosis is manifested clinically as a self-limited febrile illness, as pneumonia (acute Q fever) or as a chronic illness with endocarditis being its major complication. The recent Netherlands Q fever outbreak has driven the bacterium from a relatively cryptic, underappreciated, “niche” microorganism on the sideline of bacteriology, to one of possibly great impact on public health. Advances in the study of this microorganism proceeded slowly, primarily due to the, until recently, obligatory intracellular nature of the pathogen that in its virulent phase I must be manipulated under biosafety level-3 conditions. Proteomic studies, in particular, have generated a vast amount of information concerning several aspects of the bacterium such as virulence factors, detection/diagnostic and immunogenic biomarkers, inter-/intraspecies variation, resistance to antibiotics, and secreted effector proteins with significant clinical impact. The phenomenon observed following the genomics era, that of generation and accumulation of huge amount of data that ultimately end up unexploited on several databases, begins to emerge in the proteomics field as well. This review will focus on the advances in the field of C. burnetii proteomics through MS, attempting in parallel to utilize some of the proteomics findings by suggesting future directions for the improvement of Q fever diagnosis and therapy.     

On a New Method for Iterative Calculation of Crack Trajectory

A new efficient iterative procedure to follow automatically crack trajectory is suggested. Parameters of the procedure and illustrative examples are given.