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Meeting the communication needs of individuals requires an understanding of the contexts where they use materials. Addressing this objective requires an understanding of the complex contexts in which patients use such materials. A modified version of script theory can address such situations.
In the present work, we study the advertising competition of several marketing campaigns who need to determine how many resources to allocate to potential customers to advertise their products through direct marketing while taking into account that competing marketing campaigns are trying to do the same. Potential customers rank marketing campaigns according to the offers, promotions or discounts...
The paper revisits a fundamental and important concept of trackability that deals with the ability of a system to track reference commands. While drawing on the literature, on related concepts, a new treatment is provided in the context of LTI systems. Conditions are provided for checking the trackability of systems. This work may form the basis for further in-depth investigation on these concepts...
Graph-based algorithms play a crucial role in workflows for analyzing large datasets in domains such as social networks, biology, fraud detection, and sentiment analysis. There are two approaches for incorporating graph-based algorithms into these workflows. One approach is to integrate the graph algorithms into a workflow residing in a single system. The other approach is to use separate systems...
Full-text search systems, such as Elasticsearch and Apache Solr, enable document retrieval based on keyword queries. In many deployments these systems are multi-tenant, meaning distinct users' documents reside in, and their queries are answered by, one or more shared search indexes. Large deployments may use hundreds of indexes across which user documents are randomly assigned. The results of a search...
This paper addresses the estimation of pairwise supervoxel correspondences toward automatic semi-dense medical image registration. Supervoxel matching is performed through random forests (RF) with supervoxel indexes as label entities to predict matching areas in another target image. Ensuring accurate supervoxel boundary adherence requires a fine supervoxel decomposition which highly increases learning...
Intrinsic timing uncertainties present in modern hardware platforms have motivated the use of Extreme Value Theory (EVT) to timing analysis, however, the timing behaviour of a task may not entirely fulfil the necessary assumptions. To deal with this difficulty, randomisation at the hardware level has been proposed as a means of facilitating the use of statistical timing analysis. However, it has been...
Demanding for efficient compression and storage of DNA sequences has been rising with the rapid growth of DNA sequencing technologies. Existing reference-based algorithms map all patterns to regions found in the reference sequence, which lead to redundancy of incomplete similarity. This paper proposes an efficient reference-based method for DNA sequence compression that integrates FM-index and complementary...
The problem of node classification has been widely studied in a variety of network-based scenarios. In this paper, we will study the more challenging scenario in which some of the edges in a content-based network are labeled, and it is desirable to use this information in order to determine the labels of other arbitrary edges. Furthermore, each edge is associated with text content, which may correspond...
In multichannel sequential change detection, multiple sensors monitor a system in which an abrupt change occurs at some unknown time and is perceived by an unknown subset of sensors. The goal is to detect this change quickly, while controlling the rate of false alarms. In the traditional asymptotic analysis of this problem, the false alarm rate goes to 0 while all other parameters remain fixed. We...
This paper deals with the separation of music into individual instrument tracks which is known to be a challenging problem. We describe two different deep neural network architectures for this task, a feed-forward and a recurrent one, and show that each of them yields themselves state-of-the art results on the SiSEC DSD100 dataset. For the recurrent network, we use data augmentation during training...
Transform coefficient coding is a key module in modern video compression systems. Typically, a block of the quantized coefficients are processed in a pre-defined zig-zag order, starting from DC and sweeping through low frequency positions to high frequency ones. Correlation between magnitudes of adjacent coefficients is exploited via context based probability models to improve compression efficiency...
Compressed sensing is a simple and efficient technique that has a number of applications in signal processing and machine learning. In machine learning it provides answers to questions such as: "under what conditions is the sparse representation of data efficient?", "when is learning a large margin classifier directly on the compressed domain possible?", and "why does a large...
The article describes computational experiment and further research work in the area of identification of destructive information influence in social networks. The problem of distribution of suicidal content via open sources is presented. On the basis of calculations there was made a conclusions about the prospects of using the methods of information retrieval in the task of identification of the...
Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of...
In recent years, the development of Service Function Chaining (SFC) has been increasing dramatically alongside the transformation from physical machines to virtual devices in service provider networks. With the combination of Software-defined Networking and Network Function Virtualization, SFC enables deploying and managing network services rapidly and flexibly, which allows service providers to offer...
We propose a new representation for RNA structures. The new representation has important features by which we create and store an RNA database into a suffix array. We present a search algorithm that finds all matches to a given substructure in the database. Our tests show that this method is significantly faster than other existing search methods. We launch a website with the substructure search tool.
Bayesian nonparametric (BNP) models have recently become popular due to their flexibility in identifying the unknown number of clusters. However, they have difficulties handling heterogeneous data from multiple sources. Existing BNP methods either treat each of these sources independently - hence do not get benefits from the correlating information between them, or require to explicitly specify data...
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance...
The effectiveness of a code search engine is reduced when query terms do not represent the information needs properly or terms are ambiguous. As a result, many irrelevant code snippets and software artifacts are retrieved that hinder the developers reusing existing source code. In this paper, a technique named QExpandator is proposed that improves the effectiveness in code search by expanding query...
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