APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learners CN2, RIPPER, and APRIORI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance. APRIORI-SD is also compared to subgroup discovery algorithms CN2-SD and SubgroupMiner. The comparisons performed on U.K. traffic accident data show that APRIORI-SD is a competitive subgroup discovery algorithm.     

On commutativity based Edge Lean search

The problem of state space search is fundamental to many areas of computer science, such as, e.g., AI and formal methods. Often, the state space to be searched is huge, so optimizing the search is an important issue. In this paper, we consider the problem of visiting all states in the setting where transitions between states are generated by actions, and the (reachable) states are not known in advance. Some of the actions may commute, i.e., they result in the same state for every order in which they are taken. We show how to use commutativity to achieve full coverage of the states, while traversing a relatively small number of edges.     

On the conditional expectation on probability MV-Algebras with product

 In this paper the conditional expectation is defined and a variant of the martingale convergence theorem on probability MV-algebras with product is proved.     

Properties of mechanochemically pretreated precursors of doped BaTiO3 ceramics

The properties of doped BaTiO₃ ceramics prepared from mechanically activated precursors (BaCO₃, TiO₂, PbO), dopant (Sb) and admixtures (SiO₂, Al₂O₃) were investigated. Mechanical activation of the mixture 1.00 BaCO₃ + 1.15 TiO₂ + 0.10 PbO brings about an increase in its specific surface and partial amorphization of BaCO₃; Ba, O and C occur in the surface of this mixture in at least two chemically different states. BaTiO₃ obtained from the above mentioned mechanically activated precursors by high-temperature solid-state synthesis at 1100 ° C consists of homogeneous particles with a narrow particle size distribution.     

Directionality-Aware Design of Embroidery Patterns

Embroidery is a long-standing and high-quality approach to making logos and images on textiles. Nowadays, it can also be performed via automated machines that weave threads with high spatial accuracy. A characteristic feature of the appearance of the threads is a high degree of anisotropy. The anisotropic behavior is caused by depositing thin but long strings of thread. As a result, the stitched patterns convey both color and direction. Artists leverage this anisotropic behavior to enhance pure color images with textures, illusions of motion, or depth cues. However, designing colorful embroidery patterns with prescribed directionality is a challenging task, one usually requiring an expert designer. In this work, we propose an interactive algorithm that generates machine-fabricable embroidery patterns from multi-chromatic images equipped with user-specified directionality fields. We cast the problem of finding a stitching pattern into vector theory. To find a suitable stitching pattern, we extract sources and sinks from the divergence field of the vector field extracted from the input and use them to trace streamlines. We further optimize the streamlines to guarantee a smooth and connected stitching pattern. The generated patterns approximate the color distribution constrained by the directionality field. To allow for further artistic control, the trade-off between color match and directionality match can be interactively explored via an intuitive slider. We showcase our approach by fabricating several embroidery paths.     

Market orientation,restructuring and collaboration: The impact of digital design on organizational competitiveness

Design is commonly understood as a key element of products, contributing to their distinctiveness, usability and aesthetics. The success of a product is increasingly related to the user experience or the aesthetics of the user interface, meaning that design is increasingly important in the digital environment. The shift in competitive focus to the customer induced by digital design encourages companies to innovate and can also lead to changes in internal operations, market orientation and the reconfiguration of external collaboration procedures. This dimension of digital design-induced effects has to date seen very little research. The objective of this study is to investigate how digital design-induced changes in market orientation, internal restructuring and external cooperation affect firms' competitive orientation. The simultaneous equation framework was applied to a survey of 515 user interface and experience designers from France. Our results suggest that market orientation is not the only channel through which digital design influences firm competitiveness. Digital design leads to organizational change and the reconfiguration of external relationships that directly and indirectly help companies build competitive advantages and increase customer satisfaction.     

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as ‘chemo fog’. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning.

This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research.

     

Subsolidus phase equilibria in the Al2O3-CeO2-PbO and Al2O3-CeO2-RuO2 systems

The subsolidus phase equilibria in air for the Al₂O₃-CeO₂-PbO and Al₂O₃-CeO₂-RuO₂ systems were studied with the aim of obtaining information on possible interactions between a CeO₂-based solid electrolyte in solid-oxide fuel cells (SOFCs) and other oxides. No ternary compound was found in either of the systems. The tie line in the Al₂O₃-PbO-CeO₂ system is between Al₂Pb₂O₅ and the CeO₂.     

Automated building extraction from IKONOS images in suburban areas

The article addresses automatic building extraction from IKONOS images in suburban areas. In the proposed approach, we used a stereo pair of IKONOS images. Automatic photogrammetric methods of image matching were used to generate a digital surface model (DSM) and a digital elevation model. In further processing, single-image methods were used. The orthophotos of individual bands were created. The initial building mask was generated from the calculated normalized DSM (nDSM). The calculated normalized difference vegetation index and the road data extracted from the existing topographical database were used to remove vegetation and traffic surfaces. The mask was further improved with our own combination of methods based on non-linear diffusion filtering, unsupervised classification, colour segmentation and region growing. The final mask was vectorized using the Hough transform. Compared with a reference building database, 83.2% of the buildings in the test area were detected using the proposed approach with a quality percentage (how likely a building pixel produced by an automatic approach is correct) of 49.46.