Scientific literacy and informal science teaching

Two stages in research on scientific literary are identified: a composite saturation stage in which definitions covered all objects of science education and a stage where researchers focused on small manageable portions of scientific literary. Initial and continuous scientific literacy research in which the focus is on cognitive preference for science and informal science teaching is described. Underlying cognitive preference is a value preference for science. Three interdependent but clearly distinguishable forms of science teaching, formal, nonformal and informal, are described. Informal science teaching is identified as a condition for and an outcome of scientific literacy.     

Erscheinungsbasierte statistische Objekterkennung

Zusammenfassung.   Die automatische Erkennung und Lokalisation von Objekten in digitalen Bildern ist ein wesentlicher Bestandteil vieler praktisch relevanter Anwendungen. In diesem Artikel wird ein erscheinungsbasiertes Verfahren zur Erkennung starrer zwei- oder dreidimensionaler Objekte vorgestellt, dem eine statistische Modellierung zugrundeliegt. Im Gegensatz zu segmentierungsbasierten Verfahren, wie sie vor allem im Bereich der 3D-Objekterkennung eingesetzt werden, erm?glicht der erscheinungsbasierte Ansatz aufgrund der Modellierung der Intensit?tswerte oder davon abgeleiteter lokaler Merkmale eines Bildes die Erkennung komplexer Objekte. Die statistische Formulierung der Problemstellung bildet den mathematischen Kontext zur Bestimmung optimaler L?sungen. Die Form der Modellierung erlaubt neben der Einzelobjekterkennung auch die Berücksichtigung von heterogenem Bildhintergrund und Mehrobjektszenen. Die dazu ben?tigten lokalen Merkmale entstehen durch r?umlich begrenzte Transformationen des Bildes, wie beispielsweise Gabor- oder Wavelet-Transformationen. Die statistische Modellierung beschreibt die Verteilung dieser lokalen Merkmale anhand einer Dichtefunktion, die sich bei der Hintergrund- und Mehrobjektmodellierung als Mischungsverteilung der Einzelobjektverteilungen ergibt. Die Aufgabenstellungen des Erlernens und Erkennens von Objekten sind damit als Parametersch?tzprobleme formal darstellbar. Dabei werden im einen Fall die Modellparameter und im anderen Fall die Lageparameter beziehungsweise die Klassen von Objekten gesch?tzt. Die experimentelle überprüfung des Ansatzes anhand realer Objektaufnahmen durch CCD-Kameras zeigt seine Brauchbarkeit zur Erkennung von 2D- und 3D-Objekten bei homogenem und heterogenem Hintergrund. Eingegangen am 5. April 2000 / Angenommen am 20. Juli 2001     

试论“网球肘”的成因、诊断、预防与治疗

“网球肘”是网球运动中常见的一种伤病。通过研究“网球肘”的成因、诊断、预防与治疗，预防伤病的发生。     

L'experience regionale arabe en matiere d'education aux droits de l'homme dans les situations d'apprentissage informel

This article describes an informal approach to human rights education, which is anchored in reality and based on experience gained throughout the Arab region and more particularly within the Arab Institute for Human Rights. It is argued that the approach of the Institute could be adopted on a larger scale by organisations working in this domain, not only in the Arab region but also in other regions of the world. The article emphasises the essential role of governments and the importance of cooperation between the governmental and non-governmental levels in order to implement this type of human rights education in informal learning situations, and thus to disseminate a universal culture of human rights.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

From Archive,to Access,to Experience––Historical Documents as a Basis for Immersive Experiences

Libraries have been the key to preserving culture and historic legacy for centuries. One such treasure cataloged in The Pennsylvania State University (Penn State) Libraries is a collection of over 33,000 Sanborn? Fire Insurance Maps. Originally kept safe in metal drawers, the library has embarked on a journey to digitize this abundance of information, combine it with other media such as photographs, and make it accessible through a web interface. Inspired by these efforts, we accessed this information and took it to the next level. Using state of the art 3D modeling and immersive technologies, we created a historic 3D model and immersive experiences of Penn State, exemplarily for the 1922 campus. The resulting experiences can be accessed through the web but also through head mounted displays (HMDs) and mobile phones in combination with VR viewers such as the Google Cardboard. Additionally, they can be used anywhere in the world or on the campus itself as a way to enable remote and in situ experiences and learning. Immersive experiences let us connect to the past, the present and the future, and as such offer value to digital cultural heritage efforts.