In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from ...
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ISBN:
(纸本)9781467372596
In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-Nearest Neighbor Similarity Graph (k-N 2 SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.
Purpose： Applying SSCI journals of library and information science （LIS） as the research sample, we explore the feasibility of measuring academic journals＇ yearly social impact by using altmetric indicators. Desig...
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Purpose： Applying SSCI journals of library and information science （LIS） as the research sample, we explore the feasibility of measuring academic journals＇ yearly social impact by using altmetric indicators. Design/methodology/approach： Using a sample of 66 SSCI joumals in LIS published in 2013, statistics regarding journal mentions in social media and other online tools were retrieved from *** and meanwhile citation data was also collected from JCR and Scopus. Based on the method of principal component analysis, data was analyzed for associations between the altmetric and traditional metrics to demonstrate the effect ofaltmetric indicators on measuring academic j oumals＇ yearly impact. Findings： The Spearman＇s rank correlation test results show that altmetric indicators and traditional citation counts were significantly correlated, indicating that altmetrics can be used to measure a journal＇s yearly social impact. Research limitations： The time frame of data collected from *** may not be consistent with that of JCR and Scopus citation data. Practical implications： A new method is provided based on altmetrics for evaluating the social impact of academic journals, which can be applied to design new indicators of short-term journal impact. Originality value： In this paper, we have established a method for evaluating the social impact of academic journals based on altmetric indictors. Altmetrics can be complementary to traditional citation metrics in assessing a journal＇s impact within a year or even in a shorter period of time.
In this paper, an algorithm, named CCHN, is proposed to solve the partitional clustering problem. An outer chaotic mechanism with annealing strategy is introduced into the competitive Hopfield neural network to constr...
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ISBN:
(纸本)9781479983292
In this paper, an algorithm, named CCHN, is proposed to solve the partitional clustering problem. An outer chaotic mechanism with annealing strategy is introduced into the competitive Hopfield neural network to construct CCHN for expecting better opportunities of converging to the optimal solution. In addition to retain the competitive characteristics of the conventional competitive Hopfield neural network, CCHN displays a rich range of complex and flexible chaotic dynamics. The chaotic dynamics and the annealing strategy guarantee the powerful searching ability and the effective convergence of CCHN. Results simulated on clustering benchmark problems show that CCHN algorithm is more likely to find an optimal or near-optimal solution with a higher successful ratio than previous algorithms.
Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in H...
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Recommender system plays an important role in many practical applications that help users to deal with information overload and provide personalized recommendations to them. The context in which a choice is made has b...
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Newly emerging location-based online social services, such as Meetup and Douban, have experienced increased popularity and rapid growth. The classical Matrix Factorization methods usually only consider the user-item m...
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With high expansibility and efficient power usage, tiered wireless sensor networks are widely deployed in many fields as an important part of Internet of Things (IoTs). It is challenging to process range query while p...
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With high expansibility and efficient power usage, tiered wireless sensor networks are widely deployed in many fields as an important part of Internet of Things (IoTs). It is challenging to process range query while protecting sensitive data from adversaries. Moreover, most existing work focuses on privacy-preserving range query neglecting collusion attacks and probability attacks, which are more threatening to network security. In this paper, we first propose a secure range query protocol called secRQ, which not only protects data privacy, but also resists collusion attacks and probability attacks in tiered wireless sensor networks. Generalized inverse matrices and distance-based range query mechanism are used to guarantee security as well as high efficiency and accuracy. Besides, we propose the mutual verification scheme to verify the integrity of query results. Finally, both theoretical analysis and experimental results confirm the security, efficiency and accuracy of secRQ.
In this paper, by strict mathematic reasoning, we discover the relation between the similarity relation and lower approximation. Based on this relation, we design a fast algorithm to build a rule based fuzzy rough cla...
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