1,720,956 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Network intrusion detection system based on machine learning algorithm
No full textU ovom radu istražena je primjena algoritama strojnog i dubokog učenja za detekciju
upada u mrežu (engl. Intrusion Detection System, IDS). U radu je izrađena baza podataka simuliranih
napada, pregled, odabir i implementacija različitih algoritama strojnog i dubokog učenja, te evaluacija
njihove učinkovitosti za višeklasnu klasifikaciju. U bazi podataka realizirano je 5 vrsta napada: SYN flood,
UDP flood, DNS amplification, smurf napad i port scanning. Predložena je struktura hibrida CNN-a (engl.
Convolutional Neural Network) i LSTM-a (engl. Long short term memory) radi istraživanja uporabe
dobrih strana oba algoritma. Algoritmi poput CNN-a i predloženog CNN LSTM hibrida, pokazali su visoku
učinkovitost, dok je Histogram Gradient Boosting Classifier (HGBC) nadmašio ostale, ali pod cijenu
potencijalnog pretreniranja. Zaključak rada je da je hibridni pristup CNN LSTM vrlo korisna kombinacija
dvaju moćnih algoritama dubokog učenja, jedina prepreka je pronalazak optimalnih parametara.In this paper, the application of machine and deep learning algorithms for an intrusion
detection system is explored. The paper includes the creation of a simulated attack database, review,
selection and implementation of various machine and deep learning algorithms as well as the evaluation
of their multiclass classification effectiveness. 5 network attacks were succesfully recreated for use in
the database: SYN flood, UDP flood, DNS amplification, smurf attack and port scanning. A hybrid
convolutional neural network (CNN) and long short term memory (LSTM) structure is suggested, to use
the good sides of both algorithms. Algorithms such as CNN and the suggested CNN LSTM hybrid have
shown very high efficiency, while the Histogram Gradient Boosting Classifier (HGBC) surpassed all of the
evaluated algorithms, albeit at the cost of potential overfitting issues. The conclusion of the paper is that
the hybrid CNN LSTM approach is a very efficient combination of two exceptionally powerful deep
learning algorithms, while the only obstruction being the tuning of parameters
Network intrusion detection system based on machine learning algorithm
No full textU ovom radu istražena je primjena algoritama strojnog i dubokog učenja za detekciju
upada u mrežu (engl. Intrusion Detection System, IDS). U radu je izrađena baza podataka simuliranih
napada, pregled, odabir i implementacija različitih algoritama strojnog i dubokog učenja, te evaluacija
njihove učinkovitosti za višeklasnu klasifikaciju. U bazi podataka realizirano je 5 vrsta napada: SYN flood,
UDP flood, DNS amplification, smurf napad i port scanning. Predložena je struktura hibrida CNN-a (engl.
Convolutional Neural Network) i LSTM-a (engl. Long short term memory) radi istraživanja uporabe
dobrih strana oba algoritma. Algoritmi poput CNN-a i predloženog CNN LSTM hibrida, pokazali su visoku
učinkovitost, dok je Histogram Gradient Boosting Classifier (HGBC) nadmašio ostale, ali pod cijenu
potencijalnog pretreniranja. Zaključak rada je da je hibridni pristup CNN LSTM vrlo korisna kombinacija
dvaju moćnih algoritama dubokog učenja, jedina prepreka je pronalazak optimalnih parametara.In this paper, the application of machine and deep learning algorithms for an intrusion
detection system is explored. The paper includes the creation of a simulated attack database, review,
selection and implementation of various machine and deep learning algorithms as well as the evaluation
of their multiclass classification effectiveness. 5 network attacks were succesfully recreated for use in
the database: SYN flood, UDP flood, DNS amplification, smurf attack and port scanning. A hybrid
convolutional neural network (CNN) and long short term memory (LSTM) structure is suggested, to use
the good sides of both algorithms. Algorithms such as CNN and the suggested CNN LSTM hybrid have
shown very high efficiency, while the Histogram Gradient Boosting Classifier (HGBC) surpassed all of the
evaluated algorithms, albeit at the cost of potential overfitting issues. The conclusion of the paper is that
the hybrid CNN LSTM approach is a very efficient combination of two exceptionally powerful deep
learning algorithms, while the only obstruction being the tuning of parameters
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
Use of blockchain technology in industrial manufacturing processes
No full textZavršni rad pruža teorijski opis blockchain-a tehnologije zajedno s primjenom iste u industrijskim proizvodnim procesima. Na početku rada objašnjene su industrija 4.0 i 5.0 kao tematska podloga.
Spomenut je i oracle problem, zajedno s mogućim rješenjem. Analizirani su i zahtjevi primjene, odnosno „stupovi“ primjene blockchain tehnologije u industriji 4.0 i 5.0. Uočeni su i opisani izazovi primjene te navedena moguće rješenja istih. Istražene su i opisane aktualne primjene blockchain tehnologije u raznim granama gospodarstva.The bachelor’s thesis offers a theoretical description of blockchain technology, as well as its applications in industrial manufacturing processes. Foremost, the industry 4.0 and 5.0 are described as a thematic background. The oracle problem is mentioned as well as its possible solution. The foundations of applying the blockchain technology in industry 4.0 and 5.0, their „pillars“, are also analysed. Several application challenges were identified and theoretic solutions are offered. Current use cases of blockchain technology in various industries are researched and described
Network intrusion detection system based on machine learning algorithm
No full textU ovom radu istražena je primjena algoritama strojnog i dubokog učenja za detekciju
upada u mrežu (engl. Intrusion Detection System, IDS). U radu je izrađena baza podataka simuliranih
napada, pregled, odabir i implementacija različitih algoritama strojnog i dubokog učenja, te evaluacija
njihove učinkovitosti za višeklasnu klasifikaciju. U bazi podataka realizirano je 5 vrsta napada: SYN flood,
UDP flood, DNS amplification, smurf napad i port scanning. Predložena je struktura hibrida CNN-a (engl.
Convolutional Neural Network) i LSTM-a (engl. Long short term memory) radi istraživanja uporabe
dobrih strana oba algoritma. Algoritmi poput CNN-a i predloženog CNN LSTM hibrida, pokazali su visoku
učinkovitost, dok je Histogram Gradient Boosting Classifier (HGBC) nadmašio ostale, ali pod cijenu
potencijalnog pretreniranja. Zaključak rada je da je hibridni pristup CNN LSTM vrlo korisna kombinacija
dvaju moćnih algoritama dubokog učenja, jedina prepreka je pronalazak optimalnih parametara.In this paper, the application of machine and deep learning algorithms for an intrusion
detection system is explored. The paper includes the creation of a simulated attack database, review,
selection and implementation of various machine and deep learning algorithms as well as the evaluation
of their multiclass classification effectiveness. 5 network attacks were succesfully recreated for use in
the database: SYN flood, UDP flood, DNS amplification, smurf attack and port scanning. A hybrid
convolutional neural network (CNN) and long short term memory (LSTM) structure is suggested, to use
the good sides of both algorithms. Algorithms such as CNN and the suggested CNN LSTM hybrid have
shown very high efficiency, while the Histogram Gradient Boosting Classifier (HGBC) surpassed all of the
evaluated algorithms, albeit at the cost of potential overfitting issues. The conclusion of the paper is that
the hybrid CNN LSTM approach is a very efficient combination of two exceptionally powerful deep
learning algorithms, while the only obstruction being the tuning of parameters
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