Pemanfaatan Aplikasi Mind Map Sebagai Media Inovatif Dalam Pembelajaran Mata Kuliah Pengembangan Pendidikan Kewarganegaraan Sekolah Dasar

This study was conducted to develop an innovative medium in learning Elementary School-Civic Education (PKn SD) Development course in order to improve the lecturer competency in developing good learning media, improving learning quality, and producing a computer and internet-based media product. The development of innovative media in this study was carried out by utilizing mind map application available in internet. This study was taken place in Elementary School Teacher Education, Pedagogic Faculty, Semarang State University. The subject of research consisted of corresponding students and lecturers. This study was an educational research with research and development approach. The procedure of research included preliminary study with descriptive qualitative approach, media development design, design validation and design improvement, and followed with limited trial on media design by applying experimental method (before-after), and product revision, if any, and final product development. The result achieved in this study was that the author had conducted a preliminary study by mapping competency and indicator into PKn SD Development course and then designed the lecturing learning plan as needed by utilizing the mind map application as learning media. The author had designed an innovative media using mind map application and had the media design validated by the competent validators in their area. The mind map application-based learning media had been tried out in treatment class. The result of test with the data processed by the author showed the following result. 1) The result of pretest and posttest showed significant improvement; it could be seen from the number of correct answer minimally 5 (40%) and maximally 13 (63%) in pre-test result and minimally 16 (80%) and maximally 20 (100%) in posttest result. 2) The result of content quality and objective aspect questionnaire before and after using mind map application showed the ratio of 55%: 89%, with instructional quality aspect of 54% : 88%, and technical quality aspect of 51% : 90%, so that the overall ratio was 54% : 89%. Considering the result of data analysis, it could be found that the mind map application use can be developed further as an innovative learning media utilizing computer technology and informatics technology mastery support. The author organized the result of study into a scientific article to be published and organized a final report as accountability

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods

Sociologists associate the spatial variation of crime within an urban setting, with the concept of collective efficacy. The collective efficacy of a neighborhood is defined as social cohesion among neighbors combined with their willingness to intervene on behalf of the common good. Sociologists measure collective efficacy by conducting survey studies designed to measure individuals' perception of their community. In this work, we employ the curated data from a survey study (ground truth) and examine the effectiveness of substituting costly survey questionnaires with proxies derived from social media. We enrich a corpus of tweets mentioning a local venue with several linguistic and topological features. We then propose a pairwise learning to rank model with the goal of identifying a ranking of neighborhoods that is similar to the ranking obtained from the ground truth collective efficacy values. In our experiments, we find that our generated ranking of neighborhoods achieves 0.77 Kendall tau-x ranking agreement with the ground truth ranking. Overall, our results are up to 37% better than traditional baselines.Comment: 10 pages, 7 figure

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Simulations of planet-disc interactions using Smoothed Particle Hydrodynamics

We have performed Smoothed Particle Hydrodynamics (SPH) simulations to study the time evolution of one and two protoplanets embedded in a protoplanetary accretion disc. We investigate accretion and migration rates of a single protoplanet depending on several parameters of the protoplanetary disc, mainly viscosity and scale height. Additionally, we consider the influence of a second protoplanet in a long time simulation and examine the migration of the two planets in the disc, especially the growth of eccentricity and chaotic behaviour. One aim of this work is to establish the feasibility of SPH for such calculations considering that usually only grid-based methods are adopted. To resolve shocks and to prevent particle penetration, we introduce a new approach for an artificial viscosity, which consists of an additional artificial bulk viscosity term in the SPH-representation of the Navier-Stokes equation. This allows for an accurate treatment of the physical kinematic viscosity to describe the shear, without the use of an artificial shear viscosity.Comment: 11 pages, 14 figures, uses natbib.sty and aa.cls; accepted for publication by A&A. Please contact the author for a version with high-resolution figure

Low Frequency (100-600 MHz) Searches with Axion Cavity Haloscopes

We investigate reentrant and dielectric loaded cavities for the purpose of extending the range of axion cavity haloscopes to lower masses, below the range where the Axion Dark Matter eXperiment (ADMX) has already searched. Reentrant and dielectric loaded cavities were simulated numerically to calculate and optimize their form factors and quality factors. A prototype reentrant cavity was built and its measured properties were compared with the simulations. We estimate the sensitivity of axion dark matter searches using reentrant and dielectric loaded cavities inserted in the existing ADMX magnet at the University of Washington and a large magnet being installed at Fermilab.Comment: 33 pages, 24 figure

Search for the Cosmic Axion Background with ADMX

We report the first result of a direct search for a Cosmic ${\it axion}$ Background C$a$B - a relativistic background of axions that is not dark matter - performed with the axion haloscope, the Axion Dark Matter eXperiment (ADMX). Conventional haloscope analyses search for a signal with a narrow bandwidth, as predicted for dark matter, whereas the C$a$B will be broad. We introduce a novel analysis strategy, which searches for a C$a$B induced daily modulation in the power measured by the haloscope. Using this, we repurpose data collected to search for dark matter to set a limit on the axion photon coupling of the C$a$B originating from dark matter decay in the 800-995 MHz frequency range. We find that the present sensitivity is limited by fluctuations in the cavity readout as the instrument scans across dark matter masses. Nevertheless, we demonstrate that these challenges can be surmounted with the use of superconducting qubits as single photon counters, and allow ADMX to operate as a telescope searching for axions emerging from the decay of dark matter. The daily modulation analysis technique we introduce can be deployed for various broadband RF signals, such as other forms of a C$a$B or even high-frequency gravitational waves.Comment: 9 pages, 4 figure

Intra-week spatial-temporal patterns of crime

Since its original publication, routine activity theory has proven most instructive for understanding temporal patterns in crime. The most prominent of the temporal crime patterns investigated is seasonality: crime (most often assault) increases during the summer months and decreases once routine activities are less often outside. Despite the rather widespread literature on the seasonality of crime, there is very little research investigating temporal patterns of crime at shorter time intervals such as within the week or even within the day. This paper contributes to this literature through a spatial-temporal analysis of crime patterns for different days of the week. It is found that temporal patterns are present for different days of the week (more crime on weekends, as would be expected) and there is a spatial component to that temporal change. Specifically, aside from robbery and sexual assault at the micro-spatial unit of analysis (street segments) the spatial patterns of crime changed. With regard to the spatial pattern changes, we found that assaults and theft from vehicle had their spatial patterns change in predictable ways on Saturdays: assaults increased in the bar district and theft from vehicles increased in the downtown and recreational car park areas