1,720,991 research outputs found
Privacy-preserving distributed machine learning for artificial intelligence of things
Get PDFThis thesis proposes machine learning algorithms that can be fully distributed over ad-hoc networks of machines/agents. Developing distributed algorithms for artificial intelligence is necessary since running machine-learning-based data analytics on a single central hub may be unfeasible due to computing/communication costs. In the context of distributed learning, privacy violation risks due to curious members of the network or eavesdroppers make the development of privacy-preserving distributed algorithms imperative.
The main contributions of the thesis are around developing distributed machine learning algorithms for artificial intelligence of things including distributed algorithms with intrinsic privacy-preserving properties. In particular, the contributions can be grouped in the following categories:
• distributed learning over networks with horizontal/row partitioning of data
• intrinsically privacy-preserving distributed learning with zeroth-order optimization
• distributed learning over networks with vertical/feature partitioning of data.
In the context of distributed learning with horizontal partitioning of data, we propose a new distributed algorithm to solve the total least-squares (TLS) problem and a privacy-preserving distributed algorithm to minimize a regularized empirical risk function when the first-order information is not available.
We show that the latter algorithm has intrinsic privacy-preserving properties. Most existing privacy-preserving distributed optimization/estimation algorithms exploit some perturbation mechanism to preserve privacy, which comes at the cost of reduced accuracy. Contrarily, we exploit the inherent randomness due to the use of a zeroth-order method and show that this stochasticity is sufficient to ensure differential privacy. Moreover, we demonstrate that the proposed algorithm outperforms the existing differentially-private ones in terms of accuracy while yielding similar privacy guarantees.
In the context of distributed learning with feature partitioning of data, we develop a new distributed algorithm to solve the ridge regression problem. Subsequently, we develop a new algorithm that is designed for an ‘2-norm-square cost function with non-smooth regularizers. Finally, we develop a new consensus-based distributed algorithm for solving learning problems when the data is distributed among agents in feature partitions and computing the conjugate of the possibly non-smooth cost or regularizer functions is challenging or unfeasible. The proposed algorithm is designed for optimizing generic non-smooth objective functions over arbitrary graphs without using or computing any conjugate function. All the above-mentioned algorithms are fully-distributed and based on the alternating direction method of multipliers (ADMM) that is suitable for distributed optimization thanks to its scalability and robustness properties. We prove theoretically that the proposed algorithms converge. We also confirm their network-wide convergence via simulations
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
A Novel Microcellular Propagation Model Based on Uniform Theory of Diffraction and Physical Optics [Elektronisk resurs]
No full textA Novel Microcellular Propagation Model Based on Uniform Theory of Diffraction and Physical Optics
No full textDispelling 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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