Zobrazeno 1 - 10
of 266
pro vyhledávání: '"Huang Yushan"'
Publikováno v:
Redai dili, Vol 42, Iss 9, Pp 1500-1512 (2022)
The crime of transnational human trafficking significantly impacts social stability and state relations, and thus, deserves in-depth research from a geographical perspective. Based on 2008-2022 case data on the transnational trafficking of women in A
Externí odkaz:
https://doaj.org/article/2e15e885269c499db53a13756fd855c5
Publikováno v:
Redai dili, Vol 42, Iss 9, Pp 1443-1461 (2022)
The paper compares and discusses human trafficking from the perspective of state governance in these two countries; the five different aspect of strategies, laws and regulations, working institutions, police systems, and victim rescue and resettlemen
Externí odkaz:
https://doaj.org/article/149ab05132a5414399185841c133300b
Publikováno v:
Redai dili, Vol 42, Iss 9, Pp 1571-1584 (2022)
This study aimed to observe the sociodemographic characteristics of Chinese senior officials who have committed duty crimes as well as the spatial-temporal heterogeneity of their work place and the social network between the offenders. Further, this
Externí odkaz:
https://doaj.org/article/74bafe930ba54843bca892b233100eab
Publikováno v:
Redai dili, Vol 42, Iss 9, Pp 1513-1522 (2022)
Trafficking crime has a long history, violating personal safety and disrupting social order. At present, most relevant studies focus on the places that supply victims of trafficking. Research on the places that create demand is insufficient. As a typ
Externí odkaz:
https://doaj.org/article/8c868990d4f346f3a6638900e5c780eb
Publikováno v:
Redai dili, Vol 41, Iss 5, Pp 892-905 (2021)
Crime geography, as a new interdisciplinary subject in academia, has attracted extensive attention. Based on the "Web of ScienceTM Core Collection" and CNKI data sources, this study clarifies the thematic structure and development of crime geography
Externí odkaz:
https://doaj.org/article/9e82953a8e714e0d90610ce43ce8de0d
The personalization of machine learning (ML) models to address data drift is a significant challenge in the context of Internet of Things (IoT) applications. Presently, most approaches focus on fine-tuning either the full base model or its last few l
Externí odkaz:
http://arxiv.org/abs/2403.15905
Microcontroller Units (MCUs) are ideal platforms for edge applications due to their low cost and energy consumption, and are widely used in various applications, including personalized machine learning tasks, where customized models can enhance the t
Externí odkaz:
http://arxiv.org/abs/2403.08040
Autor:
Huang, Yushan, Haddadi, Hamed
Machine learning (ML) is moving towards edge devices. However, ML models with high computational demands and energy consumption pose challenges for ML inference in resource-constrained environments, such as the deep sea. To address these challenges,
Externí odkaz:
http://arxiv.org/abs/2305.18954
Autor:
Huang, Yushan, Zhao, Yuchen, Capstick, Alexander, Palermo, Francesca, Haddadi, Hamed, Barnaghi, Payam
Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data. Such methods do not generalize well when applied to multivariate, mul
Externí odkaz:
http://arxiv.org/abs/2302.11654
In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indicate the occurrence of healthcare-related events. Three different types
Externí odkaz:
http://arxiv.org/abs/2210.01736