1,721,078 research outputs found
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
移動ロボットにおける環境の分節化とグラフベース地図の構築
Full text link九州工業大学博士学位論文 学位記番号:生工博甲第86号 学位授与年月日:平成20年3月25日1 Introduction||
2 Overview of Related Studies||
3 Task Segmentation using mnSOM and Clustering||
4 Formation of Graph-based Maps||
5 Experimental Results on Task Segmentation and Clustering||
6 Experimental Results on the Formation of Graph basedMaps||
7 Conclusions and Discussions||
Bibliography(Abstract)
A new approach in Artificial Intelligence (AI), which focuses on agent’s interaction
with the world, is expected to solve difficulties in the classical AI. The
interaction leads an agent to exhibit emergent behaviors, which are not preprogrammed
by a designer. This is what biological agents (i.e. animals and
humans) do in their daily life. This dissertation aims at finding mechanisms
necessary for this. A mobile robot is used as a test bed for this purpose.
The real world is completely different from a virtual world frequently used
in the classical AI. The real world is always subject to complexity, noise,
and nonlinearity. Information on the real world is often spatio-temporal
in nature, and is hard to do information processing in real time. Solving
real world problems often leads to unsatisfactory results due to its inherent
difficulties. One promising approach to this is to segment the spatio-temporal
information into meaningful elements. The purpose of the present thesis is
to segment the world and to form a graph-based map for efficient processing.
Task segmentation in navigation of a mobile robot based on sensory signals
is important for realizing efficient navigation, hence attracted wide attention.
In this research, a new approach to segmentation in a mobile robot
by a modular network SOM (mnSOM) is proposed. In a mobile robot, the
standard mnSOM is not applicable as it is, because it is based on an assumption
that class labels are known a priori. In a mobile robot, however, only
a sequence of data without segmentation is available. Hence, we propose to
decompose it into many subsequences, supposing that a class label does not
change within a subsequence. Accordingly, training of mnSOM is done for
each subsequence in contrast to that for each class in the standard mnSOM.
The resulting mnSOM demonstrates segmentation performance of 94.05%
for a novel dataset based on an unrealistic assumption that winner modules
corresponding to subsequences in the same class share the same label. Since
this is not at all practical, the current study proposes segmentation without
this unrealistic assumption.
Firstly, the conventional hierarchical clustering is applied to the resulting
mnSOM. Without the above unrealistic assumption, its segmentation performance
deteriorates by only 1.2%. Hierarchical clustering assumes that the
distances between any pair of modules are provided with precision, but this
is not the case in mnSOM. Accordingly, this is followed by a clustering based
on only the distance between spatially adjacent modules with modification
by their temporal contiguity. This clustering with spatio-temporal contiguity
provides superior performance to the conventional hierarchical clustering.
Based on the resulting mnSOM, a graph-based map is formed. Due to
stochastic character of sensory-motor information, I propose to use Hidden
Markov models (HMMs) instead of a deterministic method. Given a sequence
of data, mnSOM produces sequence of labels, which may includes
erroneous ones due to noise. HMMs are employed for better estimates of
labels. Finally, from the resulting sequence of labels, L-junctions and Tjunctions
are located, and are used as nodes for constructing a graph-based
map. For comparative study, vector quantization of sensory-motor signals is
also tried. The resulting HMMs based on the quantized data also generate a
graph-based map.
The resulting graph-based map also contributes to goal seeking. Simulation
result shows that the resulting graph-based map is efficient for goal
seeking, since it is not necessary to construct a new map every time the
environment changes
Penyelesaian Persoalan Invers Kinematik Pada Manipulator Redundant Dengan Menggunakan Metode Dekomposisi
Full text linkManipulator yang dipergunakan dalam proses industri umumnya enam derajat kebebasan, sehingga dapat mencapai semua posisi orientasi end-effector. Namun manipulator ini masih keterbatasan, antara lain karena adanya kemungkinan manipulator akan menempuh konfigurasi singular. Oleh karena itu manipulator yang memiliki derajat kebebasan berlebih (redundant). Penyelesaian matematis dari persoalan invers kinematik untuk manipulator redundant umumnya ditempuh dengan melakukan perhitungan pseudoinverse dari matrik Jacobian. Perhitungan pseudoinverse ini memiliki kompleksitas perhitungan yang tinggi sehingga banyak memakan waktu dan merugikan pada implementasi time. Pada tugas akhir ini dikembangkan suatu metode baru untuk menyelesaikan persoalan invers kinematik pada manipulator redundant tanpa melalui perhitungan pseudoinverse dan memiliki kinerja yang lebih ba1k dibanding metoda terdahulu. Setelah diuji validitas serta efisiensinya, selanjutnya hasilnya akan disimulasikan dalam program komputer
Segmentation of Environment and Formation of Graph-based Maps in Mobile Robots
No full text1 Introduction||2 Overview of Related Studies||3 Task Segmentation using mnSOM and Clustering||4 Formation of Graph-based Maps||5 Experimental Results on Task Segmentation and Clustering||6 Experimental Results on the Formation of Graph basedMaps||7 Conclusions and Discussions||BibliographyA new approach in Artificial Intelligence (AI), which focuses on agent’s interaction with the world, is expected to solve difficulties in the classical AI. The interaction leads an agent to exhibit emergent behaviors, which are not preprogrammed by a designer. This is what biological agents (i.e. animals and humans) do in their daily life. This dissertation aims at finding mechanisms necessary for this. A mobile robot is used as a test bed for this purpose. The real world is completely different from a virtual world frequently used in the classical AI. The real world is always subject to complexity, noise, and nonlinearity. Information on the real world is often spatio-temporal in nature, and is hard to do information processing in real time. Solving real world problems often leads to unsatisfactory results due to its inherent difficulties. One promising approach to this is to segment the spatio-temporal information into meaningful elements. The purpose of the present thesis is to segment the world and to form a graph-based map for efficient processing. Task segmentation in navigation of a mobile robot based on sensory signals is important for realizing efficient navigation, hence attracted wide attention. In this research, a new approach to segmentation in a mobile robot by a modular network SOM (mnSOM) is proposed. In a mobile robot, the standard mnSOM is not applicable as it is, because it is based on an assumption that class labels are known a priori. In a mobile robot, however, only a sequence of data without segmentation is available. Hence, we propose to decompose it into many subsequences, supposing that a class label does not change within a subsequence. Accordingly, training of mnSOM is done for each subsequence in contrast to that for each class in the standard mnSOM. The resulting mnSOM demonstrates segmentation performance of 94.05% for a novel dataset based on an unrealistic assumption that winner modules corresponding to subsequences in the same class share the same label. Since this is not at all practical, the current study proposes segmentation without this unrealistic assumption. Firstly, the conventional hierarchical clustering is applied to the resulting mnSOM. Without the above unrealistic assumption, its segmentation performance deteriorates by only 1.2%. Hierarchical clustering assumes that the distances between any pair of modules are provided with precision, but this is not the case in mnSOM. Accordingly, this is followed by a clustering based on only the distance between spatially adjacent modules with modification by their temporal contiguity. This clustering with spatio-temporal contiguity provides superior performance to the conventional hierarchical clustering. Based on the resulting mnSOM, a graph-based map is formed. Due to stochastic character of sensory-motor information, I propose to use Hidden Markov models (HMMs) instead of a deterministic method. Given a sequence of data, mnSOM produces sequence of labels, which may includes erroneous ones due to noise. HMMs are employed for better estimates of labels. Finally, from the resulting sequence of labels, L-junctions and T-junctions are located, and are used as nodes for constructing a graph-based map. For comparative study, vector quantization of sensory-motor signals is also tried. The resulting HMMs based on the quantized data also generate a graph-based map. The resulting graph-based map also contributes to goal seeking. Simulation result shows that the resulting graph-based map is efficient for goal seeking, since it is not necessary to construct a new map every time the environment changes.九州工業大学博士学位論文 学位記番号:生工博甲第86号 学位授与年月日:平成20年3月25日平成19年
Segmentation of Environment and Formation of Graph-based Maps in Mobile Robots
No full text九州工業大学博士学位論文（要旨） 学位記番号:生工博甲第86号 学位授与年月日:平成20年3月25
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
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