1,720,982 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
Optimized hardware accelerators for data mining applications
Get PDFData mining plays an important role in a variety of fields including bioinformatics, multimedia, business intelligence, marketing, and medical diagnosis. Analysis of today’s
huge and complex data involves several data mining algorithms including clustering and
classification. The computational complexity of machine learning and data mining algorithms, that are frequently used in today’s applications such as embedded systems, makes the design of efficient hardware architectures for these algorithms a challenging issue for the development of such systems. The aim of this work is to optimize the performance of hardware acceleration for data mining applications in terms of speed and area. Most of the previous accelerator architectures proposed in the literature have been obtained using ad hoc techniques that do not allow for design space exploration, some did not consider the size (number of samples) and dimensionality (number of features in each sample) of the datasets. To obtain practical architectures that are amenable for hardware implementation, size and dimensionality of input datasets are taken into consideration in this work. For one-dimensional data, algorithm-level optimizations are investigated to design a fast and area-efficient hardware accelerator for clustering one-dimensional datasets using the well-known K-Means clustering algorithm. Experimental results show that the optimizations adopted in the proposed architecture result in faster convergence of the algorithm using less hardware resources while maintaining the quality of clustering results. The computation of similarity distance matrices is one of the computational kernels that are generally required by several machine learning and data mining algorithms to measure the degree of similarity between data samples. For these algorithms, distance calculation is considered a computationally intensive task that accounts for a significant portion of the processing time. A systematic methodology is presented to explore the design space of 2-D and 1-D processor array architectures for similarity distance computation involved in processing datasets of different sizes and dimensions. Six 2-D and six 1-D processor array architectures are developed systematically using linear scheduling and projection operations. The obtained
architectures are classified based on the size and dimensionality of input datasets, analyzed in terms of speed and area, and compared with previous architectures in the literature. Motivated by the necessity to accommodate large-scale and high-dimensional data, nonlinear scheduling and projection operations are finally introduced to design a scalable processor array architecture for the computation of similarity distance matrices. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step.Graduate2019-12-3
Fast prime field arithmetic using novel large integer representation
Get PDFLarge integers are used in several key areas such as RSA (Rivest-Shamir-Adleman) public-key cryptographic system and elliptic curve public-key cryptographic system. To achieve higher levels of security requires larger key size and this becomes a limiting factor in prime finite field GF(p) arithmetic using large integers because operations on large integers suffer from the long carry propagation problem. Large integer representation has direct impact on the efficiency of the calculations and the hardware and software implementations. Attempts to use different representations such as residue number systems suffer from their own problems. In this dissertation, we propose a novel and efficient attribute-based large integer representation scheme capable of efficiently representing the large integers that are commonly used in cryptography such as the five NIST primes and the Pierpont primes used in supersingular isogeny Diffie-Hellman (SIDH) used in post-quantum cryptography. Moreover, we propose algorithms for this new representation to perform arithmetic operations such as conversions from and to binary representation, two’s complement, left-shift, numbers comparison, addition/subtraction, modular addition/subtraction, modular reduction, multiplication, and modular multiplication. Extensive numerical simulations and software implementations are done to verify the performance of the new number representation. Results show that the attribute-based large integer arithmetic operations are done faster in our proposed representation when compared with binary and residue number representations. This makes the proposed representation suitable for cryptographic applications on embedded systems and IoT devices with limited resources for better security level.Graduate2020-07-0
Design of multi-core dataflow cryptprocessor
Get PDFEmbedded multi-core systems are implemented as systems-on-chip that rely on packet store-and-forward networks-on-chip for communications. These systems do not use buses nor global clock. Instead routers are used to move data between the cores and each core uses its own local clock. This implies concurrent asynchronous computing. Implementing algorithms in such systems is very much facilitated using dataflow concepts. In this work, we propose a methodology for implementing algorithms on dataflow platforms. The methodology can be applied to multi-threaded, multi-core platforms or a combination of these platforms as well. This methodology is based on a novel dataflow graph representation of the algorithm.
We applied the proposed methodology to obtain a novel dataflow multi-core computing model for the secure hash algorithm-3. The resulting hardware was implemented in FPGA to verify the performance parameters. The proposed model of computation has advantages such as flexible I/O timing in term of scheduling policy, execution of tasks as soon as possible, and self-timed event-driven system. In other words, I/O timing and correctness of algorithm evaluation are dissociated in this work. The main advantage of this proposal is the ability to dynamically obfuscate algorithm evaluation to thwart side-channel attacks without having to redesign the system. This has important implications for cryptographic applications.
Also, the dissertation proposes four countermeasure techniques against side-channel attacks for SHA-3 hashing. The countermeasure techniques are based on choosing stochastic or deterministic input data scheduling strategies. Extensive simulations of the SHA-3 algorithm and the proposed countermeasures approaches were performed using object-oriented MATLAB models to verify and validate the effectiveness of the techniques. The design immunity for the proposed countermeasures is assessed.Graduate2020-11-1
VHDL Implementation and Performance Analysis of two Division Algorithms
Get PDFDivision is one of the most fundamental arithmetic operations and is used extensively in engineering, scientific, mathematical and cryptographic applications. The implementation of arithmetic operation such as division, is complex and expensive in hardware. Unlike addition and subtraction, division requires several iterative computational steps on given operands to produce the result. Division, in the past has often been perceived as an infrequently used operation and received not as much attention but it is one of the most difficult operations in computer arithmetic. The techniques of implementation in hardware of such an iterative computation impacts the speed, the area and power of the digital circuit. For this reason, we consider two division algorithms based on their step size in shift. Algorithm 1 operates on fixed shift step size and has a fixed number of iteration while the Algorithms 2 operates on variable shift step size and requires considerably fewer number of iterations. In this thesis, technique is provided to save power and speed up the overall computation. It also looks at different design goal strategies and presents a comparative study to asses how each of the two design perform in terms of area, delay and power [email protected]
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
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