A k × n array with entries from an "alphabet" A = { 0, 1, ..., q - 1 } of size q is said to form a t-covering array (resp. orthogonal array) if each t × n submatrix of the array contains, among its columns, at least one (resp. exactly one) occurrence of each t-letter word from A (we must thus have n = qt for an orthogonal array to exist and n ≥ qt for a t -covering array). In this paper, we continue the agenda laid down in Godbole et al. (2009) in which the notion of consecutive covering arrays was defined and motivated; a detailed study of these arrays for the special case q = 2, has also carried out by the same authors. In the present article we use first a Markov chain embedding method to exhibit, for general values of q, the probability distribution function of the random variable W = Wk, n, t defined as the number of sets of t consecutive rows for which the submatrix in question is missing at least one word. We then use the Chen-Stein method (Arratia et al., 1989, 1990) to provide upper bounds on the total variation error incurred while approximating L (W) by a Poisson distribution Po (λ) with the same mean as W. Last but not least, the Poisson approximation is used as the basis of a new statistical test to detect run-based discrepancies in an array of q-ary data.

Abstract: Superacidic mesoporous silica materials containing embedded heteropolyacids (HPAs) were synthesized by sol–gel method in acidic media. In these materials, HPAs were immobilized into the silica structure covalently. The most acidic materials were obtained at the use of Pluronic P123 as a non-ionic pore-forming agent. Ionic surfactants also formed mesoporous structures, however, their interaction with HPA reduced acidity of the products. Obtained materials were tested as heterogeneous catalysts in liquid-phase alkylation of 1,3,5-trimethylbenzene by 1-decene. The most effective catalyst demonstrated higher conversion of starting substances to long-chain isomeric alkylbenzenes as compared to the activity of zeolite HY, a well-known alkylation catalyst. No leaching of HPA from silica gel was observed after the alkylation.

Web services discovery is the process of finding the right Web services that best match the end-users’ functional and non-functional requirements. Artificial intelligence, natural language processing, data mining, and text mining techniques have been applied by researchers in Web services discovery to facilitate the process of matchmaking. This thesis contributes to the area of Web services discovery and recommendation, adopting the Design Science Research Methodology to guide the development of useful knowledge, including design theory and artifacts. The lack of a comprehensive review of Web services discovery and recommendation in the literature motivated us to conduct a systematic literature review. Our main purpose in conducting the systematic literature review was to identify and systematically compare current clustering and association rules techniques for Web services discovery and recommendation by providing answers to various research questions, investigating the prior knowledge, and identifying gaps in the related literature. We then propose a conceptual model and a typology of Web services discovery systems. The conceptual model provides a high-level representation of Web services discovery systems, including their various elements, tasks, and relationships. The proposed typology of Web services discovery systems is composed of five groups of characteristics: storage and location characteristics, formalization characteristics, matchmaking characteristics, automation characteristics, and selection characteristics. We reference the typology to compare Web services discovery methods and architectures from the extant literature by linking them to the five proposed characteristics. We employ the proposed conceptual model with its specified characteristics to design and develop the multi-layer data mining architecture for Web services discovery using word embedding and clustering techniques. The proposed architecture consists of five layers: Web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the Web services documents. Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for Web services representation in the second layer. Then in the third layer, four distance measures, including Cosine, Euclidean, Minkowski, and Word Mover, are studied to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between Web services documents. Finally, in the fifth layer, three clustering algorithms, including affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated to cluster Web services based on the observed documents’ similarities. We demonstrate how each component of the five layers is employed in the process of Web services clustering using random-ly selected Web services documents. We conduct experimental analysis to cluster Web services using a collected dataset of Web services documents and evaluating their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better compared to those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering Web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different words representations models and syntactic and semantic similarity measures, the affinity propagation clustering technique performed better in discovering similarities among Web services.

Network embedding or representation learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization. In addition to the complex network structures, nodes may have rich non structure information such as labels and contents. Therefore, structure, label and content constitute different aspects of the entire network system that reflect node similarities from multiple complementary facets. This thesis focuses on multifaceted network embedding learning, which aims to efficiently incorporate distinct aspects of information such as node labels and node contents for cooperative low-dimensional representation learning together with node topology. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Brazil is one of the largest biomass producers in the world, thus it has a huge potential for biogas production across all its territory. Nowadays, biogas production remains largely unexplored, representing just a small fraction of its potential. The adoption of biogas technologies has grown over the past years, but it is unevenly distributed across Brazilian states. This master thesis investigates the conditions under which the widespread diffusion of biogas technologies can be enabled in the Brazilian context by looking at the factors that influence the adoption of biogas technologies and why it differs across the Brazilian territory. Technological innovation systems (TIS), societal embedding, and diffusion of innovation theory are combined in the theoretical framework to create a broad understanding of the diffusion process of biogas technologies in Brazil. While TIS focusses on what are the functions been performed within the system, Societal embedding contributes to the understanding of why technological diffusion may not happen in the same way in different regions and how technologies are rooted in society. Diffusion of innovation theory adds to the importance of individual choices and strategies in the adoption of technologies. A comparative case study was design between the states São Paulo and Paraná. 16 semi-structured interviews served as the main research instrument with the support of document studies. When looking at the factors that could impact the adoption of biogas technologies the presence of specialized actors that can offer technical support to the implementation of projects locally proved to be positive considering that biogas technologies are still novel in Brazil. The unreliability of the energy grid in rural regions also favours the adoption of biogas technologies for electricity generation in agriculture properties that can combined waste treatment with energy security. Access to financial and human resources is still the largest barrier for the diffusion of biogas technologies. Financial institutions are at large unprepared to offer good conditions for the implementation of biogas projects, mainly because they do not understand the singularities of these projects. The adoption of biogas technologies in the case studies was mainly dictated by the economic activities in place, which shaped the view on biogas technologies. The complexities of the regulatory environment in Brazil could explain why electricity generation is still the main application of biogas technologies as the electricity market is regulated at national level while gas markets are the responsibility of individual states. When biogas technologies are portraited as a tool for sustainable development, other advantages of these technologies are highlighted – environmental and social benefits such as waste treatment and job creation – creating a better claim for biogas technologies which could boost adoption.

abstract: Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges. My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

abstract: Playlists have become a significant part of the music listening experience today because of the digital cloud-based services such as Spotify, Pandora, Apple Music. Owing to the meteoric rise in usage of playlists, recommending playlists is crucial to music services today. Although there has been a lot of work done in playlist prediction, the area of playlist representation hasn't received that level of attention. Over the last few years, sequence-to-sequence models, especially in the field of natural language processing have shown the effectiveness of learned embeddings in capturing the semantic characteristics of sequences. Similar concepts can be applied to music to learn fixed length representations for playlists and the learned representations can then be used for downstream tasks such as playlist comparison and recommendation. In this thesis, the problem of learning a fixed-length representation is formulated in an unsupervised manner, using Neural Machine Translation (NMT), where playlists are interpreted as sentences and songs as words. This approach is compared with other encoding architectures and evaluated using the suite of tasks commonly used for evaluating sentence embeddings, along with a few additional tasks pertaining to music. The aim of the evaluation is to study the traits captured by the playlist embeddings such that these can be leveraged for music recommendation purposes. This work lays down the foundation for analyzing music playlists and learning the patterns that exist in the playlists in an end-to-end manner. This thesis finally concludes with a discussion on the future direction for this research and its potential impact in the domain of Music Information Retrieval. / Dissertation/Thesis / Masters Thesis Computer Science 2019

Gender bias, a sociological issue, has attracted the attention of scholars working on natural language processing (NLP) in recent years. It is confirmed that some NLP techniques like word embedding could capture gender bias in natural language. Here, we investigate gender bias in Chinese word embeddings. Gender bias tests originally designed for English are adapted and applied to Chinese word embeddings trained with three different embedding models. After verifying the efficiency of the adapted tests, the changes of gender bias throughout several time periods are tracked and analysed. Our results validate the feasibility of bias test adaptation and confirm that word embedding trained by a  model with character-level information captures more gender bias in general. Moreover, we build a possible framework for diachronic research of gender bias.

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The introduction of artificial intelligence techniques in the retail market is making a revolution in shopping experience. It allows shoppers to walk into a store, grab what they want and simply walk out without scanning barcodes or having to stand in long queues. That is what we call cashierless stores. In this project, it aims to provide an efficient solution to the automatic retail product identification. This solution presents an artifact one can use to build an end-to-end smart system for cashierless stores. Henceforth, a solution based on text classification is proposed to recognize and identify the products. For that, deep learning techniques are used such as RNN and LSTM to build the classifier. The performance of this classifier is evaluated using various metrics and it shows its efficiency with an accuracy exceeding 86%.