Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.Comment: To be published in EMNLP 201

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

Chemically stable ceramic-metal composite membrane for hydrogen separation

A hydrogen permeation membrane is provided that can include a metal and a ceramic material mixed together. The metal can be Ni, Zr, Nb, Ta, Y, Pd, Fe, Cr, Co, V, or combinations thereof, and the ceramic material can have the formula: BaZr.sub.1-x-yY.sub.xT.sub.yO.sub.3-.delta. where 0.ltoreq.x.ltoreq.0.5, 0.ltoreq.y.ltoreq.0.5, (x+y)\u3e0; 0.ltoreq..delta..ltoreq.0.5, and T is Sc, Ti, Nb, Ta, Mo, Mn, Fe, Co, Ni, Cu, Zn, Ga, In, Sn, or combinations thereof. A method of forming such a membrane is also provided. A method is also provided for extracting hydrogen from a feed stream

Multimodal Analogical Reasoning over Knowledge Graphs

Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance. Code and datasets are available in https://github.com/zjunlp/MKG_Analogy.Comment: Accepted by ICLR 202

A Sinteractive Ni-BaZr\u3csub\u3e0.8\u3c/sub\u3eY\u3csub\u3e0.2\u3c/sub\u3eO\u3csub\u3e3-δ\u3c/sub\u3e Composite Membrane for Hydrogen Separation

BaZr0.8Y0.2O3−δ (BZY) is an excellent candidate material for hydrogen permeation membranes due to its high bulk proton conductivity, mechanical robustness, and chemical stability in H2O- and CO2-containing environments. Unfortunately, the use of BZY as a separation membrane has been greatly restrained by its highly refractory nature, poor grain boundary proton conductivity, high number of grain boundaries resulting from limited grain growth during sintering, as well as low electronic conductivity. These problems can be resolved by the fabrication of a Ni–BZY composite membrane with large BZY grains, which requires the development of a sinteractive Ni–BaZr0.8Y0.2O3−δ materials system. In this work, Ni–BZY composite membranes have been fabricated by three methods: (i) a combined EDTA-citric method, (ii) a solid state reactive sintering method, and (iii) a solid state reaction method. The effects of different fabrication methods on the sintering activity, microstructure, and phase composition have been systematically investigated by dilatometry, scanning electron microscopy, and powder X-ray diffraction. After reduction, only Ni–BZY membranes prepared through the solid state reaction method were observed to be dense with large BZY grains (1 μm). It has been found that the densification and grain growth of Ni–BZY composite membranes were controlled by the method and sequence of NiO introduction during composite membrane processing. After process optimization, a 0.44 mm-thick Ni–BZY dense composite membrane was fabricated using the solid state reaction method which exhibited a hydrogen flux of 4.3 × 10−8 mol cm−2 s−1 in wet 40% H2 at 900 °C, significantly higher than those of non-BaCeO3-based hydrogen separation membranes