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In the age of big data, many graph algorithms are now required to operate in external memory and deliver performance that does not significantly degrade with the scale of the problem. One particular area that frequently deals with graphs larger than RAM is triangle listing, where the algorithms must carefully piece together edges from multiple partitions to detect cycles. In recent literature, two...
The predominance of conventional polymer processes (CPP) is undoubtedly formidable regarding monetary benefits gained in lieu of mass production in the global market. In this research, profitable commercial status of CPP is critically questioned in case of a specific large-scale application in low quantities, considering the following three factors: cost, time, and complexity. This research paper...
[Background] Security risk assessment methods in industry mostly use a tabular notation to represent the assessment results whilst academic works advocate graphical methods. Experiments with MSc students showed that the tabular notation is better than an iconic graphical notation for the comprehension of security risks. [Aim] We investigate whether the availability of textual labels and terse UML-style...
In this paper, we present a graph search approach for identifying arbitrarily complex structural genomic variation. Our method leverages the ability of long reads (e.g. from Pacific Biosciences platforms) to span multiple breakpoints of complicated local rearrangements, allowing us to resolve small-scale complexities that may be overlooked by other tools. We applied our method to a subset of NA12878...
The problem of completing low-tubal-rank tensors from incomplete noisy observations is studied. To recover the underlying tensor, an iterative singular tube thresholding (ISTT) algorithm is proposed. To explore the statistical performance of the proposed algorithm, the estimation error in terms of the Frobenius norm is upper bounded non-asymptotically. The minimax optimal lower bound of the estimation...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
Convolutional Neural Networks are being studied to provide features such as real time image recognition. One of the key operations to support HW implementations of this type of network is the multiplication. Despite the high number of operations required by Convolutional Neural Networks, they became feasible in the past years due the high availability of computing power, present on devices such as...
Due to the advances of wireless sensor networks, radiofrequency identification (RFID) and Web-based services, large volume of devices have been interconnected to the Internet of Things (IoT). In addition, the tremendous number of IoT services provided by service providers arises an urgent need to propose effective recommendation methods to discover suitable services to users. In this paper, we propose...
In this paper, we compared the differences of the consumer health language use between people who are D/deaf and hard of hearing (D/hh), and the public. Due to their relatively limited e-health literacy, D/hh people might use less complex health texts than the general public in their online communication. To test the hypothesis, we utilized text mining and readability measurements to compare the language...
Latent Dirichlet Allocation (LDA) has been widely used in text mining to discover topics from documents. One major approach to learn LDA is Gibbs sampling. The basic Collapsed Gibbs Sampling (CGS) algorithm requires O(NZ) computations to learn an LDA model with Z topics from a corpus containing N tokens. Existing approaches that improve the complexity of CGS focus on reducing the factor Z. In this...
Congestion presents a significant challenge in ad hoc networks due to their unstructured and distributed nature. In most congestion detection schemes for such networks, the affected node itself detects whether it is congested or not. The detection approach proposed in this paper performs detection with information estimators from neighbouring uncongested nodes that may be able to relieve the congestion...
In given paper offered methods and algorithms of determination of complexity of test questions for formation a database system of the adaptive test control for objective estimation of knowledge of students (pupils) in the course of training learning systems.
The eye structure of insects, which is called a compound eye, has interesting advantages. It has a large field of view, low aberrations, compact size, short image processing time, and an infinite depth of field. If we can design a compound eye camera which mimics the compound eye structure of insects, compound images with these interesting advantages can be obtained. In this paper, we consider the...
This article examines the problems of distance learning and one of their possible solution. An adaptive learning system is described that allows to provide an individual approach when passing a course of study. An example of the response of an adaptive learning system to the actions of students while performing tasks and its proposal for a level change is given.
Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Material-based computation is treated as an optimisation...
Histogram of Oriented Gradient (HOG) is a popular feature description for the purpose of object detection. However, HOG algorithm requires a high performance system because of its complex operation set. In HOG algorithm, the cell histogram generation is one of the most complex part, it uses inverse tangent, square, square root, floating point multiplication. In this paper, we propose an accurate and...
Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level...
Background: Software effort estimates are necessary and critical at an early phase for decision makers to establish initial budgets, and in a government context to select the most competitive bidder for a contract. The challenge is that estimated software requirements is the only size information available at this stage, compounded with the newly increasing adoption of agile processes in the US DoD...
Deterrence is badly needed in the cyber domain but it is hard to be achieved. Why is conventional deterrence not working effectively in the cyber domain? What specific characteristics should be considered when deterrence strategies are developed in this man-made domain? These are the questions that this paper intends to address. The research conducted helps to reveal what cyber deterrence can do and...
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as the direction with the lowest Kolmogorov complexity. This notion is very powerful as it can detect any causal dependency that can be explained by a physical process. However, due to the halting problem, it is also not computable. In this paper we propose an computable...
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