FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition

This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while ensuring accuracy, we propose an emotion recognizer augmented by a full-attention topic regularizer, which enables an emotion-related global view when modeling the local context in a conversation. A joint topic modeling strategy is introduced to implement regularization from both representation and loss perspectives. To avoid over-regularization, we drop the constraints on prior distributions that exist in traditional topic modeling and perform probabilistic approximations based entirely on attention alignment. Experiments show that our models obtain more favorable results than state-of-the-art models, and gain convincing robustness under three types of adversarial attacks

Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification

Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security

Intégration automatique de données tabulaires dans des entrepôts de données

Business Intelligence (BI) plays an important role in companies to support decision making processes. Nowadays, small companies, organizations or even individuals can exploit numerous data. However, the lack of experts prevents them from carrying BI projects out. It is thus necessary to automate the BI design process to make BI accessible for everyone. In BI architectures, data are integrated into Data Warehouses (DWs) usually modeled in a multidimensional way. Yet, tabular data widely exist in small enterprises, organizations and in the open data world. As a result, we intend to automate the DW design from tabular data. Automatic DW design from tabular data requires the detection of different multidimensional components (facts, dimensions, hierarchies...). In case of multiple sources, several DWs may be generated. If they share common information, it is necessary to merge them as one integrated DW. During DW merging, missing data imputation should be carried out to achieve a better data analysis. Therefore, we propose a solution composed of three parts: (i) automatic DW design, (ii) automatic DW merging and (iii) dimensional data imputation.Automatic DW design from tabular data is composed of measure detection and dimension detection for constructing facts and dimensions, respectively. For measure detection, we propose a machine learning-based approach that extracts three categories of features from numerical columns. Dimension detection includes functional dependency-based hierarchy detection and the distinction of parameters and weak attributes based on syntactic and semantic rules. We carry out experiments to validate that our approach is able to detect measures and different dimension elements with high effectiveness and efficiency.For automatically merging DWs, we propose a process at both the schema and instance levels, consisting of level merging, hierarchy merging, dimension merging and star schema merging. Our approach takes the different DW structure elements into account. Moreover, our approach considers different cases and may generate star or constellation schemas. We conduct experiments to validate that our DW merging solution can correctly merge DWs at both schema and instance levels.Finally, to address dimensional missing data, we propose a hybrid imputation approach named Hie-OLAPKNN that combines a hierarchical imputation (Hie) and a K-nearest neighbors-based imputation (OLAPKNN). Hierarchical imputation is based on functional dependencies between hierarchy levels and is launched first. The remaining missing data can then be completed by OLAPKNN, which applies a specific dimension instance distance and considers hierarchy dependency constraints. Our experiments show that Hie-OLAPKNN outperforms other approaches in terms of effectiveness, efficiency and respect of hierarchy strictness.La Business Intelligence (BI) joue un rôle important dans les entreprises pour soutenir les processus de prise de décision. Aujourd'hui, les petites entreprises, les organisations ou même les particuliers peuvent exploiter de nombreuses données. Cependant, le manque d'experts les empêche de mener à bien des projets de BI. Il est donc nécessaire d'automatiser le processus de conception et d’implémentation de systèmes de BI afin de le rendre accessible à tous. Dans les architectures BI, les données sont intégrées dans des entrepôts de données (EDs) généralement modélisés de manière multidimensionnelle. De plus, les données tabulaires sont largement répandues dans les petites entreprises, les organisations et dans le monde des données ouvertes. Par conséquent, nous avons l'intention d'automatiser la conception d’EDs multidimensionnels à partir de données tabulaires sans connaissance à priori des schémas.La conception automatique d’EDs à partir de données tabulaires nécessite la détection de différents composants multidimensionnels (faits, dimensions, hiérarchies...). En cas de sources multiples, plusieurs EDs peuvent être générés. S'ils partagent des informations communes, il est nécessaire de les fusionner en un seul ED intégré. Pendant la fusion d’EDs, l'imputation de données manquantes doit être effectuée pour permettre une analyse de données de meilleure qualité. Par conséquent, nous proposons une solution composée de trois parties : (i) la conception automatique d’EDs, (ii) la fusion automatique d’EDs et (iii) l'imputation de données multidimensionnelles.La conception automatique d’EDs à partir de données tabulaires comprend la détection de mesure et la détection de dimension pour définir respectivement le fait et les dimensions. Pour la détection de mesures, nous proposons une approche basée sur l'apprentissage automatique qui extrait trois catégories de caractéristiques. La détection de dimensions comprend la détection de hiérarchies (basée sur des dépendances fonctionnelles) et la distinction des paramètres et des attributs faibles (basée sur des règles syntaxiques et sémantiques). Nous avons réalisé des expérimentations pour valider que notre approche est capable de détecter les mesures et les différents éléments de dimension avec une efficacité et une efficience élevées.Concernant la fusion automatique d’EDs, nous proposons un processus basé sur les schémas et les instances, composé de la fusion de niveaux, la fusion de hiérarchies, la fusion de dimensions et la fusion de schémas en étoile. Les expérimentations ont permis de valider notre solution de fusion d’EDs.Enfin, pour traiter les données manquantes multidimensionnelles, nous proposons une approche d'imputation hybride appelée Hie-OLAPKNN qui combine une imputation hiérarchique (Hie) et une imputation basée sur les K-voisins les plus proches (OLAPKNN). L'imputation hiérarchique est basée sur les dépendances fonctionnelles entre les niveaux hiérarchiques. OLAPKNN applique une distance d'instances de dimension et tient compte des contraintes de dépendance hiérarchique. Nos expérimentations montrent que Hie-OLAPKNN surpasse les autres approches en termes d'efficacité, d'efficience et de respect des contraintes hiérarchiques

Intégration automatique de données tabulaires dans des entrepôts de données

Business Intelligence (BI) plays an important role in companies to support decision making processes. Nowadays, small companies, organizations or even individuals can exploit numerous data. However, the lack of experts prevents them from carrying BI projects out. It is thus necessary to automate the BI design process to make BI accessible for everyone. In BI architectures, data are integrated into Data Warehouses (DWs) usually modeled in a multidimensional way. Yet, tabular data widely exist in small enterprises, organizations and in the open data world. As a result, we intend to automate the DW design from tabular data. Automatic DW design from tabular data requires the detection of different multidimensional components (facts, dimensions, hierarchies...). In case of multiple sources, several DWs may be generated. If they share common information, it is necessary to merge them as one integrated DW. During DW merging, missing data imputation should be carried out to achieve a better data analysis. Therefore, we propose a solution composed of three parts: (i) automatic DW design, (ii) automatic DW merging and (iii) dimensional data imputation.Automatic DW design from tabular data is composed of measure detection and dimension detection for constructing facts and dimensions, respectively. For measure detection, we propose a machine learning-based approach that extracts three categories of features from numerical columns. Dimension detection includes functional dependency-based hierarchy detection and the distinction of parameters and weak attributes based on syntactic and semantic rules. We carry out experiments to validate that our approach is able to detect measures and different dimension elements with high effectiveness and efficiency.For automatically merging DWs, we propose a process at both the schema and instance levels, consisting of level merging, hierarchy merging, dimension merging and star schema merging. Our approach takes the different DW structure elements into account. Moreover, our approach considers different cases and may generate star or constellation schemas. We conduct experiments to validate that our DW merging solution can correctly merge DWs at both schema and instance levels.Finally, to address dimensional missing data, we propose a hybrid imputation approach named Hie-OLAPKNN that combines a hierarchical imputation (Hie) and a K-nearest neighbors-based imputation (OLAPKNN). Hierarchical imputation is based on functional dependencies between hierarchy levels and is launched first. The remaining missing data can then be completed by OLAPKNN, which applies a specific dimension instance distance and considers hierarchy dependency constraints. Our experiments show that Hie-OLAPKNN outperforms other approaches in terms of effectiveness, efficiency and respect of hierarchy strictness.La Business Intelligence (BI) joue un rôle important dans les entreprises pour soutenir les processus de prise de décision. Aujourd'hui, les petites entreprises, les organisations ou même les particuliers peuvent exploiter de nombreuses données. Cependant, le manque d'experts les empêche de mener à bien des projets de BI. Il est donc nécessaire d'automatiser le processus de conception et d’implémentation de systèmes de BI afin de le rendre accessible à tous. Dans les architectures BI, les données sont intégrées dans des entrepôts de données (EDs) généralement modélisés de manière multidimensionnelle. De plus, les données tabulaires sont largement répandues dans les petites entreprises, les organisations et dans le monde des données ouvertes. Par conséquent, nous avons l'intention d'automatiser la conception d’EDs multidimensionnels à partir de données tabulaires sans connaissance à priori des schémas.La conception automatique d’EDs à partir de données tabulaires nécessite la détection de différents composants multidimensionnels (faits, dimensions, hiérarchies...). En cas de sources multiples, plusieurs EDs peuvent être générés. S'ils partagent des informations communes, il est nécessaire de les fusionner en un seul ED intégré. Pendant la fusion d’EDs, l'imputation de données manquantes doit être effectuée pour permettre une analyse de données de meilleure qualité. Par conséquent, nous proposons une solution composée de trois parties : (i) la conception automatique d’EDs, (ii) la fusion automatique d’EDs et (iii) l'imputation de données multidimensionnelles.La conception automatique d’EDs à partir de données tabulaires comprend la détection de mesure et la détection de dimension pour définir respectivement le fait et les dimensions. Pour la détection de mesures, nous proposons une approche basée sur l'apprentissage automatique qui extrait trois catégories de caractéristiques. La détection de dimensions comprend la détection de hiérarchies (basée sur des dépendances fonctionnelles) et la distinction des paramètres et des attributs faibles (basée sur des règles syntaxiques et sémantiques). Nous avons réalisé des expérimentations pour valider que notre approche est capable de détecter les mesures et les différents éléments de dimension avec une efficacité et une efficience élevées.Concernant la fusion automatique d’EDs, nous proposons un processus basé sur les schémas et les instances, composé de la fusion de niveaux, la fusion de hiérarchies, la fusion de dimensions et la fusion de schémas en étoile. Les expérimentations ont permis de valider notre solution de fusion d’EDs.Enfin, pour traiter les données manquantes multidimensionnelles, nous proposons une approche d'imputation hybride appelée Hie-OLAPKNN qui combine une imputation hiérarchique (Hie) et une imputation basée sur les K-voisins les plus proches (OLAPKNN). L'imputation hiérarchique est basée sur les dépendances fonctionnelles entre les niveaux hiérarchiques. OLAPKNN applique une distance d'instances de dimension et tient compte des contraintes de dépendance hiérarchique. Nos expérimentations montrent que Hie-OLAPKNN surpasse les autres approches en termes d'efficacité, d'efficience et de respect des contraintes hiérarchiques

Environmental sustainable development of electronic manufacturing industry : a comparative study of China, India, Japan and Singapore

As most countries’ demand for basic necessities has been satisfied since 2000, people’s focus has shifted to making resources sustainable. Another trend observed during this period is the increasingly heavy dependence on electronic products. The trend is demonstrated by the significant proportion of the countries’ GDP attributed to electronic manufacturing (EM). Being the leaders of development worldwide, China and India faces sustainability issues in EM. As the leader of Asia’s EM industry, Japan has developed unique and effective Home Appliance Regulation Laws. Singapore, the hub of R&D in EM, is adopting a totally different approach in the sustainable electronic manufacturing (SEM). In this report, we examine the difficulties in the current status of policies and regulations regarding green manufacturing (GM) and electronic waste (E-waste) recycling practices in EM industries of China, India, Japan and Singapore. Our study reveals that Japan has well-established regulations and the manufacturers are responsible for E-waste recycling and minimizing resources used in production system. On the other hand, China’s and India’s electronic manufacturers are less responsible for GM even though the government has implemented regulations that deal with sustainability issues. It is also worthwhile to note that Singapore’s manufacturers are responsible for SEM practices as well as engages companies that specialize in E-waste recycling, even without formal regulations from the government. After the earthquake in Japan in March 2011, it can be assumed that the EM industry is affected, and many new regulations will be implemented. Also, future trends in consumers’ taste and how it would affect manufacturers’ decisions are discussed in the report.BUSINES

意大利基于全纳教育理念的学校建筑设计策略 The School Building Design Strategies Based on Inclusive Education in Italy

文章通过归纳梳理意大利特殊教育及建筑的相关政策法案，研究其教育理念与模式的发展以及全纳教育对学生特殊 教育需求的关注；通过对意大利全纳中小学校的调研、对建筑师和相关研究人员的访谈、及相关文献的研究，从适应教学方 法的总体布局、满足学生个别化与多样化需求的空间设计以及帮助克服身心障碍的环境设计三方面剖析意大利全纳学校中满 足残障学生特殊教育需求的建筑设计策略。以期为我国相关政策与标准的制定、招收残障学生学校的规划设计提供参考。 关键词　意大利中小学校　全纳教育　特殊教育需求　总体布局　空间环境设计By summarizing the relevant educational and architectural policies and legislations related to the special education in Italy, this paper investigates the development of the educational concepts and model, and the attention of Inclusive Education to special education needs of students. Through investigation of Italian inclusive primary and secondary schools, interview with architects and relevant researchers and the study of literature, it also analyses the architectural design strategies of Italian inclusive schools that could meet the special educational needs of disabled students from three aspects: overall layout to adapt teaching method, spatial design to meet differentiated and diversified needs of students, and environmental design to help overcome physical and psychological obstacles. It intends to provide a reference for the draw up of related policies and codes, and the planning and design of schools that enroll SEN students

Attribute-Based Equality Test over Encrypted Data without Random Oracles

© 2013 IEEE. Sensitive data would be encrypted before uploading to the cloud due to the privacy issue. However, how to compare the encrypted data efficiently becomes a problem. Public Key Encryption with Equality Test (PKEET) provides an efficient way to check whether two ciphertexts (of possibly different users) contain the same message without decryption. As an enhanced variant, Attribute-based Encryption with Equality Test (ABEET) provides a flexible mechanism of authorization on the equality test. Most of the existing ABEET schemes are only proved to be secure in the random oracle model. Their security, however, would not be guaranteed if random oracles are replaced with real-life hash functions. In this work, we propose a construction of CP-ABEET scheme and prove its security based on some reasonable assumptions in the standard model. We then show how to modify the scheme to outsource complex computations in decryption and equality test to a third-party server in order to support thin clients