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

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

Seve: Automatic tool for verification of security protocols

Master'sMASTER OF SCIENC

Textual Manifold-based Defense Against Natural Language Adversarial Examples

Recent studies on adversarial images have shown that they tend to leave the underlying low-dimensional data manifold, making them significantly more challenging for current models to make correct predictions. This so-called off-manifold conjecture has inspired a novel line of defenses against adversarial attacks on images. In this study, we find a similar phenomenon occurs in the contextualized embedding space induced by pretrained language models, in which adversarial texts tend to have their embeddings diverge from the manifold of natural ones. Based on this finding, we propose Textual Manifold-based Defense (TMD), a defense mechanism that projects text embeddings onto an approximated embedding manifold before classification. It reduces the complexity of potential adversarial examples, which ultimately enhances the robustness of the protected model. Through extensive experiments, our method consistently and significantly outperforms previous defenses under various attack settings without trading off clean accuracy. To the best of our knowledge, this is the first NLP defense that leverages the manifold structure against adversarial attacks. Our code is available at \url{https://github.com/dangne/tmd}