Cost-Sensitive Decision Tree with Multiple Resource Constraints

Resource constraints are commonly found in classification tasks. For example, there could be a budget limit on implementation and a deadline for finishing the classification task. Applying the top-down approach for tree induction in this situation may have significant drawbacks. In particular, it is difficult, especially in an early stage of tree induction, to assess an attribute’s contribution to improving the total implementation cost and its impact on attribute selection in later stages because of the deadline constraint. To address this problem, we propose an innovative algorithm, namely, the Cost-Sensitive Associative Tree (CAT) algorithm. Essentially, the algorithm first extracts and retains association classification rules from the training data which satisfy resource constraints, and then uses the rules to construct the final decision tree. The approach has advantages over the traditional top-down approach, first because only feasible classification rules are considered in the tree induction and, second, because their costs and resource use are known. In contrast, in the top-down approach, the information is not available for selecting splitting attributes. The experiment results show that the CAT algorithm significantly outperforms the top-down approach and adapts very well to available resources.Cost-sensitive learning, mining methods and algorithms, decision trees

A STUDY ON DEVELOPMENT AND CURRENT APPLICATION OF MOTION GRAPHIC IN TAIWAN’S POPULAR MUSIC

With the advances in technology, the way of communications has become more diverse. Motion graphic combines graphic design, animation design, and film languages. Motion graphic is a new industry with intense performance styles and can be used in different media and platforms, such as commercials, music videos, film and television titles, web pages, and various display screen sizes, etc. Because motion graphic is a non-narrative time-based media, mostly it combines with music. The Taiwan 25th Golden Melody Awards introduced motion graphic design for the first time in 2014. This changed the monotony of past awards ceremony and reignited audience’s attention and discussion, as well as sparked a wave of motion graphic within the country. Through in-depth interviews with some industry experts, this study has defined the concept of motion graphic, analyzed its applications and development in popular music, and explored its future trends. The results of the study show that motion graphic is a cross-domain integration. With the appearance of interactive technology products, motion graphic strengthens the connection between the media and the audience, bringing different interactive experiences to the audience. The development of the Internet has also led to the spread of motion graphic due to high image quality, which has changed the speed of message dissemination and the way people listen to music. Because motion graphic has the advantage of actively transmitting messages, it has three main applications in popular music, which are music videos, concert video design, and the visual presentation of award ceremony. It is an inevitable trend that future music will be presented in the form of motion graphic

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

3D Reconstruction from IR Thermal Images and Reprojective Evaluations

Infrared thermography has been widely used in various domains to measure the temperature distributions of objects and surfaces. The methodology can be further extended to 3D applications if the spatial information of the temperature distribution is available. This paper proposes a 3D infrared imaging approach based on silhouette volume intersection to reconstruct volumetric temperature data of enclosed objects. 3D IR images are taken from various angles and integrated with 2D RGB images to effectively reconstruct a 3D model of the object's temperature distributions. Various automatic thresholding methods are also compared and evaluated by reprojection scoring to systematically assess the effectiveness and accuracy of the different approaches. Experiment results have demonstrated the ability of the system to provide an estimate to the 3D location of an internal heat source from images taken externally

Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms

This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging technology which provides cloud service in the edge of multi-radio access networks aims to reduce the service latency experienced by end devices. When individual MEC systems do not have adequate resource capacity to fulfill service requests, forming MEC federations for resource sharing could provide economic incentive to MEC operators. To this end, we need to maximize social welfare in each federation, which involves efficient federation structure generations, federation profit maximization by resource provisioning configuration, and fair profit distribution among participants. We model the problem as a coalition game with difference from prior work in the assumption of latency and locality constraints and also in the consideration of various service policies/demand preferences. Simulation results show that the proposed approach always increases profits. If local requests are served with local resource with priority, federation improves profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586)

A systematic review and meta-analysis of guided tissue regeneration/osseous grafting for the treatment of Class II furcation defects

AbstractBackground/purposeThe purpose of this article was to conduct a systematic review of the clinical evidence on the efficacy of guide tissue regeneration (GTR) with/without osseous grafting (OG) in treating periodontal furcation Class II defects.Materials and methodsReports from randomized controlled clinical trials, with at least 6 months follow-up, comparing open flap debridement (OFD); GTR, and GTR + OG were located from various sources. Sources included the electronic databases of Cochrane Oral Health Group specialist trials register, MEDLINE, and PubMed; in addition, journal archives were hand-searched. Trials up to and including March 2012 were included. Using the PICO (Patient or Problem, Intervention, Comparison, and Outcome) question format, data from eligible articles were extracted and meta-analyzed. The outcomes measures were furcation closure rate, vertical/horizontal bone fill (re-entry), and vertical/horizontal attachment level gain.ResultsThe meta-analysis showed that the GTR and GTR + OG groups obtained greater furcation closure rate, vertical/horizontal bone fill, and vertical/horizontal attachment level gain than the OFD group in mandibular molars. The GTR group obtained greater vertical/horizontal bone fill and vertical attachment level gain than the OFD group in maxillary molars. The GTR + OG group achieved better clinical outcomes than the GTR group did in all the comparing outcomes in mandibular molars.ConclusionGTR technique seemed to be more effective than OFD for resolving Class II periodontal furcation defects, and the GTR + OG technique showed even better clinical results. The outcomes were better for mandibular molars than for maxillary molars