1,721,242 research outputs found
A Review of Techniques for Positioning in WLAN with Limited Data
No full textTraditionally, positioning in WLAN was associated w
ith some issues. The presence of the
multipath forced researchers to resort to fingerpri
nting based positioning techniques that
inherently require extensive site surveying and the
abundance of reference signals. However, if
we consider WiFi data for the task of crowd analysi
s, the data should be collected on the side of
the network provider and, in this case, it is usual
ly scarce. Thus, methods that require fewer
reference signals for positioning are needed. This
paper provides the comparison of WLAN
based positioning methods that can operate with a s
ingle A
WLAN Based Positioning with a Single Access Point
No full textWLAN has lately been applied to the problem of mobility tracking and behavior analysis. To further the
development of the studies in this direction the positioning system that can perform on the network side
with minimal human participation is needed. One of the current limitations is the requirement on the
number of reference signal available. Thus, methods that require fewer reference signals for positioning
are needed. This paper provides the comparison of WLAN based positioning methods that can operate with
a single AP
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
Predicting Type Annotations for Python using Embeddings from Graph Neural Networks
Full text linkAn intelligent tool for type annotations in Python would increase the productivity of developers. Python is
a dynamic programming language, and predicting types using static analysis is difficult. Existing techniques
for type prediction use deep learning models originated in the area of Natural Language Processing. These
models depend on the quality of embeddings for source code tokens. We compared approaches for pre-
training embeddings for source code. Specifically, we compared FastText embeddings to embeddings trained
with Graph Neural Networks (GNN). Our experiments showed that GNN embeddings outperformed FastText
embeddings on the task of type prediction. Moreover, they seem to encode complementary information since
the prediction quality increases when both types of embeddings are use
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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
