9,763 research outputs found

    How are topics born? Understanding the research dynamics preceding the emergence of new areas

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    The ability to promptly recognise new research trends is strategic for many stake- holders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the ‘parents’ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise

    Node Embedding over Temporal Graphs

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    In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient

    Patent Analytics Based on Feature Vector Space Model: A Case of IoT

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    The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

    Analyzing the Triad Diagnosis Requirement for Autism in Arkansas

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    Autism Spectrum Disorder (ASD) is a developmental disorder that has steadily increased in incidence and prevalence over the last ten years. While the disorder can be diagnosed as early as two years of age, most children do not receive a diagnosis until they are about five years old. The state of Arkansas requires a triad autism diagnosis, mandating that a physician, psychologist, and speech pathologist individually evaluate and agree on a diagnosis of autism for a child. The purpose of this evaluation was to examine diagnostic practices in the 11 states that participate in the Autism and Developmental Disabilities Monitoring Network (ADDM) to compare efficiency of the Arkansas triad diagnosis. The ADDM findings show that Arkansas ranks last in autism detection and evaluation of at-risk children before age 3 years. These practices have not improved in the last 10 years. The participating states with the lowest ages of diagnosis and strongest practices utilized an interdisciplinary team approach and had programs located in multiple cities that were focused solely on early identification, evaluation, and diagnosis. By implementing similar interdisciplinary practices in Arkansas, the triad diagnosis requirement could be re-evaluated, and more resources for evaluation and diagnosis can be created for the state. Arkansas’ identification, evaluation, and diagnostic rates could improve by discontinuing the triad autism diagnosis requirement in favor of implementing similar interdisciplinary team approaches and increasing program locations throughout the state
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