1,724,110 research outputs found
Semiotics of the XX-th century
No full textIvanov V. V. Semiotics of the XX-th century [Электронный ресурс] / V. V. Ivanov// Современная семиотика и гуманитарные науки / Рос. акад. наук, Ин-т славяноведения ; отв. ред. Вяч. Вс. Иванов. - М. : Яз. славян. культур, 2010. - P. 53-107
V. V. Ivanov, V. N. Toporov, Slavjanskie jazykovye modelirujushchie semioti-cheskie sistemy
No full textTodorov Tzvetan. V. V. Ivanov, V. N. Toporov, Slavjanskie jazykovye modelirujushchie semioti-cheskie sistemy. In: L'Homme, 1966, tome 6 n°2. pp. 138-139
Semiotics of the XX-th century
No full textIvanov V. V. Semiotics of the XX-th century [Электронный ресурс] / V. V. Ivanov// Современная семиотика и гуманитарные науки / Рос. акад. наук, Ин-т славяноведения ; отв. ред. Вяч. Вс. Иванов. - М. : Яз. славян. культур, 2010. - P. 53-107
Languages, Writings, Linguistic ideas, Religions, Cultures in the Grand Duchy of Lithuania and neighbouring areas in XIV–XVII c. A. D. (Sociolinguistic Situation / Polyglossia)
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Approaches for Representing Software as Graphs for Machine Learning Applications
No full textMachine learning (ML) is making its way into the source code analysis. Most of the time, this happens with the help of Natural Language Processing (NLP) techniques. However, NLP techniques often represent their input as a sequence of tokens. This assumption is reasonable when processing text because the words related to the same object usually follow each other. However, in source code, this assumption can be inadequate simply because of the source code execution nature. Graphs can be much more adequate for representing source code. They can capture the dependency structure of a program. Due to the recent advances in the area of machine learning on graphs, researchers started to explore the graph-based representation of software in the scope of machine learning applications. There is no single way to represent a program in the form of a graph. For this reason, researchers explored different alternatives, such as function call graphs (FCG), data flow graphs (DFG), control flow graphs (CFG), or their mixtures. In this survey, we overview approaches for representing software as graphs and how these representations help to solve machine learning tasks
Representing Programs with Dependency and Function Call Graphs for Learning Hierarchical Embeddings
Full text linkAny source code can be represented as a graph. This kind of representation allows capturing the interaction
between the elements of a program, such as functions, variables, etc. Modeling these interactions can enable
us to infer the purpose of a code snippet, a function, or even an entire program. Lately, more and more
work appear, where source code is represented in the form of a graph. One of the difficulties in evaluating the
usefulness of such representation is the lack of a proper dataset and an evaluation metric. Our contribution is in
preparing a dataset that represents programs written in Python and Java source codes in the form of dependency
and function call graphs. In this dataset, multiple projects are analyzed and united into a single graph. The
nodes of the graph represent the functions, variables, classes, methods, interfaces, etc. Nodes for functions
carry information about how these functions are constructed internally, and where they are called from. Such
graphs enable training hierarchical vector representations for source code. Moreover, some functions come
with textual descriptions (docstrings), which allows learning useful tasks such as API search and generation
of documentation
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