More Tourists Green Up: Bringing the Charm back to Vietnam

More and more tourists have chosen attractions in Vietnam as destinations of their travel not only to explore their hidden charms but also make them greener through their role as green value co-creators. Vietnam is filled with hidden charms for tourists’ discovery such as Sa Pa Mountains, Ha Long Bay, Phong Nha Cave, Hoi An Ancient Town, Da Nang Beach, Mekong Delta, and Phu Quoc Island. Nonetheless, these charms are increasingly less charming and losing the returns of tourists, both international and local, due to the destruction of their green. Although people and factories in the localities are the main “terminators” to the green of tourist attractions, tourists themselves also contribute to their agony. Yet, there have been more and more instances of international tourists who, on their travel journey in Vietnam, volunteered with their companions to clean a part of a beach or a riverbank. They not only “planted” the green in the destinations but also shared green values with other tourists and the local community and fanned the green flames in them. Tourists, in other words, are likely to join hands with tour companies to make tours greener and return the green to tourist attractions. Here is a snapshot of how tourists green up with tour companies

Labour shortage in Hungary: legal framework, opportunities and challenges for Vietnamese migrant workers

A COVID-19 pandémiát követő időszak gazdaság fellendülést eredményezett, ami a magyar munkaerőpiacon munkaerő hiányt idézett elő. Erre a lényeges problémára az egyik lehetséges megoldást az EU-n kívüli harmadik országból – mint például Vietnám – származó migráns munkavállalók jelenthetik. Ennek jogi alapját teremtette meg az EU és Vietnám között – évekkel korábban – létrejött kölcsönös kereskedelmi megállapodás, valamint a Vietnam és Magyarország között fennálló kölcsönös együttműködési megállapodás. A cikk áttekintést nyújt az EU, Vietnám és Magyarország közötti relációban a migráns munkavállalók jogi helyzetét érintő megállapodások kereteiről és fontosabb tartalmi elemeiről. Ugyancsak elemzi a Magyarországon kialakult munkaerőhiányból eredő lehetőségeket és megoldandó problémákat a potenciális vietnámi migráns munkavállalók számára

Balanced Scorecard Implementation at Rang Dong Plastic Joint-stock Company (RDP)

From the balanced scorecard (BSC) framework, which encourages the use of both financial and non-financial measures of performance, allowing the firm to pinpoint its strategic objectives via balancing four perspectives – financial, customers, internal business processes, and learning and growth – to measure firm performance (Kaplan and Norton, 1992; Kaplan and Norton, 1996b), the paper sought to explore how balanced business scorecards were designed and to what extent of success they were implemented at Rang Dong Plastic Joint-Stock Company (RDP) in terms of its organizational structure and company philosophy. Keywords: balanced scorecard; performance measuremen

Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learing approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by $6\%-7.5\%$ in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.Comment: WWW 201