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Sökning: WFRF:(Zhang Jin liang) > Luleå tekniska universitet

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1.
  • Cao, Liang, et al. (författare)
  • GCHAR : An efficient Group-based Context–aware human activity recognition on smartphone
  • 2018
  • Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 118:part-1, s. 67-80
  • Tidskriftsartikel (refereegranskat)abstract
    • With smartphones increasingly becoming ubiquitous and being equipped with various sensors, nowadays, there is a trend towards implementing HAR (Human Activity Recognition) algorithms and applications on smartphones, including health monitoring, self-managing system and fitness tracking. However, one of the main issues of the existing HAR schemes is that the classification accuracy is relatively low, and in order to improve the accuracy, high computation overhead is needed. In this paper, an efficient Group-based Context-aware classification method for human activity recognition on smartphones, GCHAR is proposed, which exploits hierarchical group-based scheme to improve the classification efficiency, and reduces the classification error through context awareness rather than the intensive computation. Specifically, GCHAR designs the two-level hierarchical classification structure, i.e., inter-group and inner-group, and utilizes the previous state and transition logic (so-called context awareness) to detect the transitions among activity groups. In comparison with other popular classifiers such as RandomTree, Bagging, J48, BayesNet, KNN and Decision Table, thorough experiments on the realistic dataset (UCI HAR repository) demonstrate that GCHAR achieves the best classification accuracy, reaching 94.1636%, and time consumption in training stage of GCHAR is four times shorter than the simple Decision Table and is decreased by 72.21% in classification stage in comparison with BayesNet.
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2.
  • Gehrmann, Sebastian, et al. (författare)
  • GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
  • 2022
  • Ingår i: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. - : Association for Computational Linguistics (ACL). ; , s. 266-281
  • Konferensbidrag (refereegranskat)abstract
    • Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances.We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other’s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.
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