HRM in Transition: Chinese HR Managers Talk.

This research aims to ascertain how HR managers in China perceive their roles under China's economic transition period from a state-controlled economy to a market economy. There has been a tendency for studies of Chinese HRM to be dominated by survey-based methods and quantitative techniques and whilst these have provided helpful cross-sectional insights they have generally failed to capture how the practice of HRM is experienced from the perspective of Chinese managers themselves. An examination of the literature shows that both in the context of Chinese HRM, and HRM more generally, the lack of detailed qualitative research has left gaps in terms of HR managers' perceptions of their role, most notably in terms of its emotional dimensions (especially important in the Chinese context of guanxi practices) and its status as part of a career pattern. This research therefore adopts an exploratory approach that aims to provide tentative explanations of patterns that emerge from detailed semi-structured interviews with Chinese HR managers (38 managers from 26 diverse companies). Analysis of this data revealed three groups of respondents, defined according to their HR role descriptions: Restricted Functional; Professional Functional; and Strategic Partners. These groups are compared and contrasted in terms of their HR practices, the ways in which they handle emotions, and their career anchors. In each case a distinctive pattern emerges which appears to reflect a complex combination of individual aspirations and structural factors, the latter particularly associated with the hierarchical structure of the organizations concerned and the ways in which power is exercised. The study contributes to knowledge of HRM in six ways: 1. It supports studies that claim ownership may not be the main determining factor in shaping Chinese HRM practices; 2. It shows a tension in the debates about the role of HR managers in relation to employee care and advocacy; 3. It makes a valuable contribution to the role of emotion in HR work; 4. It contributes to showing the significance of guanxi practices within Chinese organizations; 5. The study contributes to the area of HR career development which has been seen to be largely unresearched in any form; 6. Finally, it contributes to knowledge of HRM in China by filling an important gap in the form of the lack of qualitative studies of Chinese managers. By presenting a view of the nature and roles of Chinese HR work through the words of Chinese HR managers themselves, this study presents a body of rich data that provides a very unusual insight into the experiences of a group that has been widely explored from the 'outside' but has been given little opportunity to 'speak for itself

Recommending with limited number of trusted users in social networks

© 2018 IEEE. To estimate the reliability of an unknown node in social networks, existing works involve as many opinions from other nodes as possible. Though this makes it possible to approximate the real property of the unknown nodes, the computational complexity increases as the scale of social networks getting bigger and bigger. We therefore propose a novel method which involve only limited number of social relations to predict the trustworthiness of the unknown nodes. The proposed method involves four rating prediction mechanisms: FM use the recommendation given by the most reliable recommender with the shortest trust propagation distance from the active user as the predicted rating, FMW weights the recommendation in FM, FA uses the mean value of recommendations with the shortest trust propagation distance from the active user as the predicted rating, and FAW weights recommendations in FA. The simulation results show that the proposed method can greatly reduce the rating prediction calculation, while the rating prediction losses are reasonable

Mehanizam pretraživanja preporučitelja za sustave sigurnih preporučitelja u Internetu stvari

Intelligent things are widely connected in Internet of Things (IoT) to enable ubiquitous service access. This may cause heavy service redundant. The trust-aware recommender system (TARS) is therefore proposed for IoT to help users finding reliable services. One fundamental requirement of TARS is to efficiently find as many recommenders as possible for the active users. To achieve this, existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism. In this paper, we propose an efficient searching mechanism, named S_Searching: based on the scale-freeness of trust networks, choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.Inteligentni objekti su naširoko povezani u Internet stvari kako bi se omogućio sveprisutni pristup uslugama. To može imati za posljedicu veliku redundanciju usluga. Stoga je za pronalaženje pouzdane usluge u radu predložen vjerodostojan sustav preporučitelja (VSP). Temeljni zahtjev VSP-a je učinkovito pretraživanje maksimalnog mogućeg broja preporu čtelja za aktivnog korisnika. Kako bi se to postiglo, postojeći pristupi VSP-a u potpunosti pretražuju sigurnu mrežu što ima za posljedicu velike računske zahtjeve. Iako je sigurna mreža mreža bez skale, eksperimentima je pokazano kako VSP ne može naći zadovoljavajući broj preporučitelja direktnom primjenom klasičnog algoritma pretraživanja. U ovom radu je predložen učinkovit algoritam pretraživanja, nazvan S_Searching: temeljen na sigurnim mrežama bez skale koji koristi čvorove globalno najvećeg stupnja za izgradnju Skeleton-a i pretražuje preporučitelja pomoću Skeleton-a. Iskorištavanjem nadre.enih izlaznih stupnjeva čvorova Skeleton-a S_Searching može s visokom učinkovitošću pronaći preporučitelje. Eksperimentalni rezultati pokazuju kako S_Searching može naći gotovo jednak broj preporučitelja koji bi se pronašli potpunom pretragom, što je mnogo više od onoga što se postiže primjenom klasičnog algoritma pretrage na mreži bez skale, uz znatno smanjenje računske kompleksnosti i zahtjeva

Classification with class noises through probabilistic sampling

© 2017 Accurately labeling training data plays a critical role in various supervised learning tasks. Now a wide range of algorithms have been developed to identify and remove mislabeled data as labeling in practical applications might be erroneous due to various reasons. In essence, these algorithms adopt the strategy of one-zero sampling (OSAM), wherein a sample will be selected and retained only if it is recognized as clean. There are two types of errors in OSAM: identifying a clean sample as mislabeled and discarding it, or identifying a mislabeled sample as clean and retaining it. These errors could lead to poor classification performance. To improve classification accuracy, this paper proposes a novel probabilistic sampling (PSAM) scheme. In PSAM, a cleaner sample has more chance to be selected. The degree of cleanliness is measured by the confidence on the label. To accurately estimate the confidence value, a probabilistic multiple voting idea is proposed which is able to assign a high confidence value to a clean sample and a low confidence value to a mislabeled sample. Finally, we demonstrate that PSAM could effectively improve the classification accuracy over existing OSAM methods

Inheritance and identification of molecular markers associated with a novel dwarfing gene in barley

Background Dwarfing genes have widely been used in barley breeding program. More than 30 types of dwarfs or semidwarfs have been reported, but a few has been exploited in barley breeding because pleiotropic effects of dwarfing genes cause some undesired traits. The plant architecture of newly discovered dwarfing germplasm "Huaai 11" consisted of desirable agronomic traits such as shortened stature and early maturity. Genetic factor controlling the plant height in dwarf line Huaai 11 was investigated. Results The Huaai 11 was crossed with tall varieties Monker, Mpyt, Zhenongda 3, Zaoshu 3, Advance, Huadamai 1, Huadamai 6, Hyproly and Ris01508. All the F1 plants displayed tall trait. Both tall and dwarf plants appeared in all the F2 populations with a 3:1 segregation ratio, suggesting that dwarfism of Huaai 11 is controlled by a single recessive gene, btwd1. Allelism test indicated that this dwarfing gene in the Huaai 11 is nonallelic with the gene br, uzu, sdw1 and denso. Using a double haploid population derived from a cross of Huadamai 6 and Huaai 11 and SSR markers the novel dwarfing gene was mapped onto the long arm of chromosome 7H, and closely linked to Bmac031 and Bmac167 with genetic distance of 2.2 cM. Conclusion Huaai 11 is a new source of dwarf for broadening the genetic base of dwarfism. This dwarf source was controlled by a recessive dwarfing gene btwd1, was mapped onto the long arm of chromosome 7H

Re-Expression of AKAP12 Inhibits Progression and Metastasis Potential of Colorectal Carcinoma In Vivo and In Vitro

Background: AKAP12/Gravin (A kinase anchor protein 12) is one of the A-kinase scaffold proteins and a potential tumor suppressor gene in human primary cancers. Our recent study demonstrated the highly recurrent loss of AKAP12 in colorectal cancer and AKAP12 reexpression inhibited proliferation and anchorage-independent growth in colorectal cancer cells, implicating AKAP12 in colorectal cancer pathogenesis. Methods: To evaluate the effect of this gene on the progression and metastasis of colorectal cancer, we examined the impact of overexpressing AKAP12 in the AKAP12-negative human colorectal cancer cell line LoVo, the single clone (LoVo-AKAP12) compared to mock-transfected cells (LoVo-CON). Results: pCMV6-AKAP12-mediated AKAP12 re-expression induced apoptosis (3 % to 12.7%, p,0.01), migration (89.667.5 cells to 31.064.1 cells, p,0.01) and invasion (82.765.2 cells to 24.763.3 cells, p,0.01) of LoVo cells in vitro compared to control cells. Nude mice injected with LoVo-AKAP12 cells had both significantly reduced tumor volume (p,0.01) and increased apoptosis compared to mice given AKAP12-CON. The quantitative human-specific Alu PCR analysis showed overexpression of AKAP12 suppressed the number of intravasated cells in vivo (p,0.01). Conclusion: These results demonstrate that AKAP12 may play an important role in tumor growth suppression and the survival of human colorectal cancer

Improving Complex Network Controllability via Link Prediction

© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Complex network is a network structure composed of a large number of nodes and complex relationships between these nodes. Using complex network can model many systems in real life. The individual in the system corresponds to the node in the network and the relationship between these individuals corresponds to the edge in the network. The controllability of complex networks is to study how to enable the network to arrive at the desired state from any initial state by external input signals. The external input signals transmit to the whole network through some nodes in the network, and these nodes are called driver node. For the study of controllability of complex network, it is mainly to judge whether the network is controllable or not and how to select the appropriate driver nodes at present. If a network has a high controllability, the network will be easy to control. However, complex networks are vulnerable and will cause declining of controllability. Therefore, we propose in this paper a link prediction-based method to make the network more robust to different modes of attacking. Through experiments we have validated the effectiveness of the proposed method

Socialized healthcare service recommendation using deep learning

© 2018, The Natural Computing Applications Forum. Socialized recommender system recommends reliable healthcare services for users. Ratings are predicted on the healthcare services by merging recommendations given by users who has social relations with the active users. However, existing works did not consider the influence of distrust between users. They recommend items only based on the trust relations between users. We therefore propose a novel deep learning-based socialized healthcare service recommender model, which recommends healthcare services with recommendations given by recommenders with both trust relations and distrust relations with the active users. The influences of recommenders, considering both the node information and the structure information, are merged via the deep learning model. Experimental results show that the proposed model outperforms the existing works on prediction accuracy and prediction coverage simultaneously, even for cold start users or users with very sparse trust relations. It is also computational less expensive