Graduate Education and the Evolving genre of Electronic Theses and Dissertations

Electronic writing spaces are transforming graduate education—enhancing mentoring and the shape of dissertation content. In this article, we review international efforts to develop the Networked Digital Library of Theses and Dissertations (NDLTD). We examine the need to amplify access to current scholarship, the need for training materials and centralized metadata, and the need to develop multi-language search interfaces. We explore ways traditional print dissertations are being remediated by electronic writing. We analyze challenges to implementing ETD initiatives, including concerns about preservation, attitudes toward intellectual property, and the training challenges involved in deploying technology to present new research using media and interactive perspectives. We conclude that universities need to develop and support ETD initiatives to provide broader access to their research and to provide students with the training and tools they need to present their knowledge more effectively in a digital world.     

The M-computations induced by accessibility relations in nonstandard models M of Hoare logic

Hoare logic [1] is a logic used as a way of specifying semantics of programming languages, which has been extended to be a separation logic to reason about mutable heap structure [2]. In a model M of Hoare logic, each program α induces an M-computable function f _α ^M on the universe of M; and the M-recursive functions are defined on M. It will be proved that the class of all the M-computable functions f _α ^M induced by programs is equal to the class of all the M-recursive functions. Moreover, each M-recursive function is \(\sum {_1^{{N^M}}} \)-definable in M, where the universal quantifier is a number quantifier ranging over the standard part of a nonstandard model M.     

The development and promotion of electronic theses and dissertations (ETDs) within the UK

An increasing number of universities are accepting and encouraging the submission of theses and dissertations in electronic format. Two hundred and three institutions are now members of the international ‘Networked Digital Library of Theses and Dissertations’ (NDLTD), and in May 2003 over 200 individuals travelled to Berlin to participate in the ETD symposium (‘Next Steps—Electronic Theses and Dissertations Worldwide’). The support of UNESCO and the work undertaken by key institutions such as Virginia Tech. has led to wide-scale developments at national and individual level. Within the UK, funding from the Joint Information Systems Committee (JISC) has enabled three project teams to engage in research and development associated with the creation, management, and use of electronic theses and dissertations (ETDs). This paper considers recent ETD-related activity in the UK within the broader international context. It concentrates, in particular, on the work of the Electronic Theses project consortium that is led by The Robert Gordon University. The benefits of ETDs, issues of concern, the selection of software, and advocacy requirements are amongst the topics addressed. The authors welcome comments on both the project and the information available on the Electronic Theses project Web pages.     

Comment on “On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes”

Comparison of generative and discriminative classifiers is an ever-lasting topic. As an important contribution to this topic, based on their theoretical and empirical comparisons between the naïve Bayes classifier and linear logistic regression, Ng and Jordan (NIPS 841–848, 2001) claimed that there exist two distinct regimes of performance between the generative and discriminative classifiers with regard to the training-set size. In this paper, our empirical and simulation studies, as a complement of their work, however, suggest that the existence of the two distinct regimes may not be so reliable. In addition, for real world datasets, so far there is no theoretically correct, general criterion for choosing between the discriminative and the generative approaches to classification of an observation x into a class y; the choice depends on the relative confidence we have in the correctness of the specification of either p(y|x) or p(x, y) for the data. This can be to some extent a demonstration of why Efron (J Am Stat Assoc 70(352):892–898, 1975) and O’Neill (J Am Stat Assoc 75(369):154–160, 1980) prefer normal-based linear discriminant analysis (LDA) when no model mis-specification occurs but other empirical studies may prefer linear logistic regression instead. Furthermore, we suggest that pairing of either LDA assuming a common diagonal covariance matrix (LDA-Λ) or the naïve Bayes classifier and linear logistic regression may not be perfect, and hence it may not be reliable for any claim that was derived from the comparison between LDA-Λ or the naïve Bayes classifier and linear logistic regression to be generalised to all generative and discriminative classifiers.     

Human action recognition on depth dataset

Human action recognition is a hot research topic; however, the change in shapes, the high variability of appearances, dynamitic background, potential occlusions in different actions and the image limit of 2D sensor make it more difficult. To solve these problems, we pay more attention to the depth channel and the fusion of different features. Thus, we firstly extract different features for depth image sequence, and then, multi-feature mapping and dictionary learning model (MMDLM) is proposed to deeply discover the relationship between these different features, where two dictionaries and a feature mapping function are simultaneously learned. What is more, these dictionaries can fully characterize the structure information of different features, while the feature mapping function is a regularization term, which can reveal the intrinsic relationship between these two features. Large-scale experiments on two public depth datasets, MSRAction3D and DHA, show that the performances of these different depth features have a big difference, but they are complementary. Further, the features fusion by MMDLM is very efficient and effective on both datasets, which is comparable to the state-of-the-art methods.     

Attracting ellipsoids and synthesis of oscillatory regimes

An approach to stabilization of nonlinear oscillations in multidimensional spaces is proposed on the basis of the V.I. Zubov’s stability theory for invariant sets. As a special case, the derived controls make it possible to excite self-oscillating regimes in specified state subspaces R ^2k ? R ²ⁿ with simultaneous oscillation damping on Cartesian products R ^2n?2k .     

Relativizing small complexity classes and their theories

Existing definitions of the relativizations of NC ¹, L and NL do not preserve the inclusions \({{\bf NC}^1 \subseteq {\bf L}, {\bf NL}\subseteq {\bf AC}^1}\). We start by giving the first definitions that preserve them. Here for L and NL we define their relativizations using Wilson’s stack oracle model, but limit the height of the stack to a constant (instead of log(n)). We show that the collapse of any two classes in \({\{{\bf AC}^0 (m), {\bf TC}^0, {\bf NC}^1, {\bf L}, {\bf NL}\}}\) implies the collapse of their relativizations. Next we exhibit an oracle α that makes AC ^k (α) a proper hierarchy. This strengthens and clarifies the separations of the relativized theories in Takeuti (1995). The idea is that a circuit whose nested depth of oracle gates is bounded by k cannot compute correctly the (k + 1) compositions of every oracle function. Finally, we develop theories that characterize the relativizations of subclasses of P by modifying theories previously defined by the second two authors. A function is provably total in a theory iff it is in the corresponding relativized class, and hence, the oracle separations imply separations for the relativized theories.     

Mining web access patterns with super-pattern constraint

We consider the problem of mining web access patterns with super-pattern constraint. This constraint requires that the sequential patterns in the sequence database must contain a particular set of patterns as sub-patterns. One common application of this constraint is web usage mining which mines the user access behavior on the web. In this paper, we introduce an efficient strategy for mining web access patterns with super-pattern constraint that requires only one database scan. Firstly, we present the MWAPC (M ining W eb A ccess P atterns based on super-pattern C onstraint) algorithm, in which each frequent pattern has to be checked if it contains at least one pattern from a user-defined set of patterns. Then we develop an effective algorithm, called EMWAPC that prunes the search space at the beginning of mining process and avoids checking the constraints one by one based on three proposed propositions. We have conducted the experiments on real web log databases. The experimental results show that the proposed algorithms outperform the previous methods.     

Using core-periphery structure to predict high centrality nodes in time-varying networks

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree. To measure the effectiveness of our framework, we perform prediction task on a large set of diverse time-varying networks. We obtain F1-scores as high as 0.81 and 0.72 in predicting the top m closeness and betweenness centrality vertices respectively for real networks where the highly central vertices mostly reside in the innermost core. For synthetic networks that conform to this property we achieve F1-scores of 0.94 and 0.92 for closeness and betweenness respectively. We validate our results by showing that the practical effects of our predicted vertices match the effects of the actual high centrality vertices. Finally, we also provide a formal sketch demonstrating why our method works.     

The B<Subscript>4</Subscript>-valued propositional logic with unary logical connectives ~<Subscript>1</Subscript> / ~<Subscript>2</Subscript> /¬

A B ₄-valued propositional logic will be proposed in this paper which there are three unary logical connectives ~₁, ~₂, ¬ and two binary logical connectives ∧, ∨, and a Gentzen-typed deduction system will be given so that the system is sound and complete with B ₄-valued semantics, where B ₄ is a Boolean algebra.     

Algorithms of constructing a stabilizing solution of the algebraic Riccati equation based on its linear reduction in the problems of analytical construction of controllers

In the problem of the stabilizing solution of the algebraic Riccati equation, the resolvent Θ(s) = (s I _2n ? H)^?1 of the Hamilton 2n × 2n-matrix H of the algebraic Riccati equation allows us to reduce the problem to a linear matrix equation. In [1], the constructions necessary for this and the theorem of existence and representation of the stabilized solutions to an algebraic Riccati equation was proposed. In this paper, the methods of constructing the resolvent and the linear reduction matrix defined by it necessary for the application of the theorem, and in addition, the algorithms of constructing stabilizing solution of the algebraic Riccati equation are proposed.     

Fast online algorithm for nonlinear support vector machines and other alike models

Paper presents a unique novel online learning algorithm for eight popular nonlinear (i.e., kernel), classifiers based on a classic stochastic gradient descent in primal domain. In particular, the online learning algorithm is derived for following classifiers: L1 and L2 support vector machines with both a quadratic regularizer w ^t w and the l ₁ regularizer |w|₁; regularized huberized hinge loss; regularized kernel logistic regression; regularized exponential loss with l ₁ regularizer |w|₁ and Least squares support vector machines. The online learning algorithm is aimed primarily for designing classifiers for large datasets. The novel learning model is accurate, fast and extremely simple (i.e., comprised of few coding lines only). Comparisons of performances of the proposed algorithm with the state of the art support vector machine algorithm on few real datasets are shown.     

Repeated patterns detection in big data using classification and parallelism on LERP Reduced Suffix Arrays

Suffix array is a powerful data structure, used mainly for pattern detection in strings. The main disadvantage of a full suffix array is its quadratic O(n²) space capacity when the actual suffixes are needed. In our previous work [39], we introduced the innovative All Repeated Patterns Detection (ARPaD) algorithm and the Moving Longest Expected Repeated Pattern (MLERP) process. The former detects all repeated patterns in a string using a partition of the full Suffix Array and the latter is capable of analyzing large strings regardless of their size. Furthermore, the notion of Longest Expected Repeated Pattern (LERP), also introduced by the authors in a previous work, significantly reduces to linear O(n) the space capacity needed for the full suffix array. However, so far the LERP value has to be specified in ad hoc manner based on experimental or empirical values. In order to overcome this problem, the Probabilistic Existence of LERP theorem has been proven in this paper and, furthermore, a formula for an accurate upper bound estimation of the LERP value has been introduced using only the length of the string and the size of the alphabet used in constructing the string. The importance of this method is the optimum upper bounding of the LERP value without any previous preprocess or knowledge of string characteristics. Moreover, the new data structure LERP Reduced Suffix Array is defined; it is a variation of the suffix array, and has the advantage of permitting the classification and parallelism to be implemented directly on the data structure. All other alternative methodologies deal with the very common problem of fitting any kind of data structure in a computer memory or disk in order to apply different time efficient methods for pattern detection. The current advanced and elegant proposed methodology allows us to alter the above-mentioned problem such that smaller classes of the problem can be distributed on different systems and then apply current, state-of-the-art, techniques such as parallelism and cloud computing using advanced DBMSs which are capable of handling the storage and analysis of big data. The implementation of the above-described methodology can be achieved by invoking our innovative ARPaD algorithm. Extensive experiments have been conducted on small, comparable strings of Champernowne Constant and DNA as well as on extremely large strings of π with length up to 68 billion digits. Furthermore, the novelty and superiority of our methodology have been also tested on real life application such as a Distributed Denial of Service (DDoS) attack early warning system.     

Reservoir-based network traffic stream summarization for anomaly detection

Summarization is an important intermediate step for expediting knowledge discovery tasks such as anomaly detection. In the context of anomaly detection from data stream, the summary needs to represent both anomalous and normal data. But streaming data has distinct characteristics, such as one-pass constraint, for which conducting data mining operations are difficult. Existing stream summarization techniques are unable to create summary which represent both normal and anomalous instances. To address this problem, in this paper, a number of hybrid summarization techniques are designed and developed using the concept of reservoir for anomaly detection from network traffic. Experimental results on thirteen benchmark data streams show that the summaries produced from stream using pairwise distance (PSSR) and template matching (TMSSR) techniques can retain more anomalies than existing stream summarization techniques, and anomaly detection technique can identify the anomalies with high true positive and low false positive rate.     

Model-based testing for concurrent systems: unfolding-based test selection

Model-based testing has mainly focused on models where concurrency is interpreted as interleaving (like the ioco theory for labeled transition systems), which may be too coarse when one wants concurrency to be preserved in the implementation. In order to test such concurrent systems, we choose to use Petri nets as specifications and define a concurrent conformance relation named co-ioco. We present a test generation algorithm based on Petri net unfolding able to build a complete test suite w.r.t our co-ioco conformance relation. In addition, we propose several coverage criteria that allow to select finite prefixes of an unfolding in order to build manageable test suites.     

Two time-efficient gibbs sampling inference algorithms for biterm topic model

Biterm Topic Model (BTM) is an effective topic model proposed to handle short texts. However, its standard gibbs sampling inference method (StdBTM) costs much more time than that (StdLDA) of Latent Dirichlet Allocation (LDA). To solve this problem we propose two time-efficient gibbs sampling inference methods, SparseBTM and ESparseBTM, for BTM by making a tradeoff between space and time consumption in this paper. The idea of SparseBTM is to reduce the computation in StdBTM by both recycling intermediate results and utilizing the sparsity of count matrix \(\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}\). Theoretically, SparseBTM reduces the time complexity of StdBTM from O(|B| K) to O(|B| K _w ) which scales linearly with the sparsity of count matrix \(\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}\) (K _w ) instead of the number of topics (K) (K _w < K, K _w is the average number of non-zero topics per word type in count matrix \(\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}\)). Experimental results have shown that in good conditions SparseBTM is approximately 18 times faster than StdBTM. Compared with SparseBTM, ESparseBTM is a more time-efficient gibbs sampling inference method proposed based on SparseBTM. The idea of ESparseBTM is to reduce more computation by recycling more intermediate results through rearranging biterm sequence. In theory, ESparseBTM reduces the time complexity of SparseBTM from O(|B|K _w ) to O(R|B|K _w ) (0 < R < 1, R is the ratio of the number of biterm types to the number of biterms). Experimental results have shown that the percentage of the time efficiency improved by ESparseBTM on SparseBTM is between 6.4% and 39.5% according to different datasets.     

iHOME: Index-Based JOIN Query Optimization for Limited Big Data Storage

Query optimization in Big Data becomes a promising research direction due to the popularity of massive data analytical systems such as Hadoop system. The query optimization is getting hard to efficiently execute JOIN queries on top of Hadoop query language, Hive, over limited Big Data storages. According to our previous work, HiveQL Optimization for JOIN query over Multi-session Environment (HOME) system has been introduced over Hadoop system to improve its performance by storing the intermediate results to avoid repeated computations. Time overheads and Big Data storages limitation are considered the main drawback of the HOME system, especially in the case of using additional physical storages or renting extra virtualized storages. In this paper, an index-based system for reusing data called indexing HiveQL Optimization for JOIN over Multi-session Big Data Environment (iHOME) is proposed to overcome HOME overheads by storing only the indexes of the joined rows instead of storing the full intermediate results directly. Moreover, the proposed iHOME system addresses eight cases of JOIN queries which classified into three groups; Similar-to-iHOME, Compute-on-iHOME, and Filter-of-iHOME. According to the experimental results of the iHOME system using TPC-H benchmark, it is found that the execution time of eight JOIN queries using iHOME on Hive has been reduced. Also, the stored data size in the iHOME system is reduced relative to the HOME system, as well as, the Big Data storage is saved. So, by increasing stored data size, the iHOME system guarantees the space scalability and overcomes the storage limitation.