Research on the social integration of the second generation of international Chinese immigrants: Example of children of Chinese immigrants from mainland China aged 17-35 in Lisbon, Portugal

Portugal has a long history of receiving immigrants, because of the aging of the population, the country's economic activities and pension issues become more and more serious, the role of foreign immigrants in the life of Portuguese society has become increasingly important. Logically that the second generation of immigrants will gradually grow to become an important force in building Portuguese society. However, due to various reasons, such as ethnicity, economic conditions, and social climate, the social status and development of the second generation of immigrants may not be in line with that of the native youth, and there are concerns about the degree of social integration and trends in their social status. This study focuses on the second generation of Chinese immigrants aged 17 to 35 years’ old who are of mainland Chinese origin and currently living in Lisbon, Portugal, with residence status. I will try to explore the identity of the second generation of Chinese immigrants in a multicultural environment by combining the theories of comprehensive cultural identity and social integration with the characteristics of the local Chinese society and integrate data from three aspects: family environment, schooling, and community. The results show that the second generation of immigrants has a strong sense of family mission and responsibility, has a higher level of education than the previous generation, and has a positive correlation between their professional development and the level of social integration of the previous generation.Portugal tem uma longa história de acolhimento de imigrantes, devido ao envelhecimento da população, as atividades econômicas do país e as questões previdenciárias se tornam cada vez mais sérias, o papel dos imigrantes estrangeiros na vida da sociedade portuguesa tem se tornado cada vez mais importante. Logicamente que a segunda geração de imigrantes irá gradualmente crescer para se tornar uma força importante na construção da sociedade portuguesa. Entretanto, devido a várias razões, tais como etnia, condições econômicas e clima social, o status social e o desenvolvimento da segunda geração de imigrantes pode não estar de acordo com o da juventude nativa, e há preocupações sobre o grau de integração social e tendências em seu status social. Este estudo se concentra na segunda geração de imigrantes chineses de 17 a 35 anos de idade que são de origem chinesa continental e que atualmente vivem em Lisboa, Portugal, com status de residência. Vou tentar explorar a identidade da segunda geração de imigrantes chineses em um ambiente multicultural, combinando as teorias de identidade cultural abrangente e integração social com as características da sociedade chinesa local e integrar dados de três aspectos: ambiente familiar, escolaridade e comunidade. Os resultados mostram que a segunda geração de imigrantes tem um forte senso de missão e responsabilidade familiar, tem um nível de educação superior à geração anterior, e tem uma correlação positiva entre seu desenvolvimento profissional e o nível de integração social da geração anterior

KGAT: Knowledge Graph Attention Network for Recommendation

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations --- which connect two items with one or multiple linked attributes --- are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism.Comment: KDD 2019 research trac

Generalized Second Price Auction with Probabilistic Broad Match

Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported broad match between queries and bid keywords when executing GSP auctions, however, it has been revealed that GSP auction with the standard broad-match mechanism they are currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g., its theoretical properties are known only for the single-slot case and full-information setting, and even in this simple setting, the corresponding worst-case social welfare can be rather bad). To address this issue, we propose a novel broad-match mechanism, which we call the Probabilistic Broad-Match (PBM) mechanism. Different from SBM that puts together the ads bidding on all the keywords matched to a given query for the GSP auction, the GSP with PBM (denoted as PBM-GSP) randomly samples a keyword according to a predefined probability distribution and only runs the GSP auction for the ads bidding on this sampled keyword. We perform a comprehensive study on the theoretical properties of the PBM-GSP. Specifically, we study its social welfare in the worst equilibrium, in both full-information and Bayesian settings. The results show that PBM-GSP can generate larger welfare than SBM-GSP under mild conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in Bayesian setting. To the best of our knowledge, this is the first work on broad-match mechanisms for GSP that goes beyond the single-slot case and the full-information setting

Explainable Reasoning over Knowledge Graphs for Recommendation

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.Comment: 8 pages, 5 figures, AAAI-201

Kernel Density Metric Learning

This paper introduces a supervised metric learning algorithm, called kernel density metric learning (KDML), which is easy to use and provides nonlinear, probability-based distance measures. KDML constructs a direct nonlinear mapping from the original input space into a feature space based on kernel density estimation. The nonlinear mapping in KDML embodies established distance measures between probability density functions, and leads to correct classification on datasets for which linear metric learning methods would fail. Existing metric learning algorithms, such as large margin nearest neighbors (LMNN), can then be applied to the KDML features to learn a Mahalanobis distance. We also propose an integrated optimization algorithm that learns not only the Mahalanobis matrix but also kernel bandwidths, the only hyper-parameters in the nonlinear mapping. KDML can naturally handle not only numerical features, but also categorical ones, which is rarely found in previous metric learning algorithms. Extensive experimental results on various benchmark datasets show that KDML significantly improves existing metric learning algorithms in terms of kNN classification accuracy

受験者固有層を有する深層学習モデルによる複数記述式問題同時自動採点手法

近年，受験者の論理的思考力や表現力などの実践的な能力を評価する方法の一つとして，記述式問題が国内外の大規模試験を含む様々なテストで広く活用されている．一方で，特に大規模試験においては，採点の一貫性の確保が難しいことや採点に要する時間的・経済的コストが大きいことなどが記述式問題導入の課題として指摘されてきた．これらの問題を解決できる方法のひとつとして自動採点技術が近年注目されている．記述式問題自動採点（Automated short-answer grading, ASAG）とは，短答記述式問題に対する回答文を人工知能技術を用いて自動的に採点する技術である．近年では，深層学習を用いた様々な自動採点モデルが提案され，高い精度を達成している．一般に深層学習自動採点モデルを含む従来の自動採点モデルは，記述式問題ごとに個別に収集されたデータセットを用いて訓練・構築され，問題ごとに独立に自動採点が行われる．しかし，現実のテストでは，同一テスト上で複数の記述式問題が出題されることがしばしばあり，そのような同一テスト上の問題群は受験者の特定の潜在的特性を測定するように設計されていると考えられる．そのような場合，複数の記述式問題におけるある受験者の得点は問題ごとに独立ではなく，受験者の特性という共通の要因に依存すると考えられる．したがって，複数の記述式問題の背後にある受験者固有の潜在特性を推定できれば，それは各問題に対する得点予測の有益な補助情報として機能すると期待できる．そこで，本研究では，同一テスト上に同一の潜在特性を測定する複数の記述式問題が出題される場合を対象に，各受験者の複数の回答文からその受験者に固有の潜在特性を推定し，それを得点予測に活用する機構を持つ新たな深層学習自動採点モデルを提案する．提案モデルは，複数の短答式問題に対する回答文を同時に入力し，それらに対応する複数の得点を同時に出力する多入力多出力型の深層学習モデルとして定式化する．また，本研究では，実データを用いた提案モデルの有効性評価実験から次のことを示した．1）受験者固有の特徴量を加味することで得点予測の精度を改善できた．2）BERTベースの提案モデルは，一定の訓練を受けた人間評価者による採点と同程度の精度を達成した．3）提案モデルで抽出される受験者固有の特徴量は，数理モデルを用いたテスト理論の一つである項目反応理論に基づいて得点データから推定される受験者の能力値と相関しており，測定対象の受験者の能力を反映していると解釈できた．電気通信大学202

Privacy protection for e-health systems by means of dynamic authentication and three-factor key agreement

During the past decade, the electronic healthcare (e-health) system has been evolved into a more patient-oriented service with smaller and smarter wireless devices. However, these convenient smart devices have limited computing capacity and memory size, which makes it harder to protect the user’s massive private data in the e-health system. Although some works have established a secure session key between the user and the medical server, the weaknesses still exist in preserving the anonymity with low energy consumption. Moreover, the misuse of biometric information in key agreement process may lead to privacy disclosure, which is irreparable. In this study, we design a dynamic privacy protection mechanism offering the biometric authentication at the server side whereas the exact value of the biometric template remains unknown to the server. And the user anonymity can be fully preserved during the authentication and key negotiation process because the messages transmitted with the proposed scheme are untraceable. Furthermore, the proposed scheme is proved to be semantic secure under the Real-or-Random Model. The performance analysis shows that the proposed scheme suits the e-health environment at the aspect of security and resource occupation

Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network

Knowledge Graphs (KGs) are becoming increasingly essential infrastructures in many applications while suffering from incompleteness issues. The KG completion task (KGC) automatically predicts missing facts based on an incomplete KG. However, existing methods perform unsatisfactorily in real-world scenarios. On the one hand, their performance will dramatically degrade along with the increasing sparsity of KGs. On the other hand, the inference procedure for prediction is an untrustworthy black box. This paper proposes a novel explainable model for sparse KGC, compositing high-order reasoning into a graph convolutional network, namely HoGRN. It can not only improve the generalization ability to mitigate the information insufficiency issue but also provide interpretability while maintaining the model's effectiveness and efficiency. There are two main components that are seamlessly integrated for joint optimization. First, the high-order reasoning component learns high-quality relation representations by capturing endogenous correlation among relations. This can reflect logical rules to justify a broader of missing facts. Second, the entity updating component leverages a weight-free Graph Convolutional Network (GCN) to efficiently model KG structures with interpretability. Unlike conventional methods, we conduct entity aggregation and design composition-based attention in the relational space without additional parameters. The lightweight design makes HoGRN better suitable for sparse settings. For evaluation, we have conducted extensive experiments-the results of HoGRN on several sparse KGs present impressive improvements (9% MRR gain on average). Further ablation and case studies demonstrate the effectiveness of the main components. Our codes will be released upon acceptance.Comment: The manuscript under revie