Earth observation (EO) data provenance is vital for facilitating data sharing and cooperative processing. However, existing techniques for managing EO data provenance still have various weaknesses, including decentralization, traceability, transparency, tamper-proofing, and security protection. Despite being a transformative solution in various domains, the potential of blockchain technology in EO data provenance remains largely unexplored. This article introduces a blockchain-based solution for EO data provenance, aiming to facilitate data sharing and traceability. We have implemented a prototype based on the blockchain technology and conducted a performance evaluation. To the best of authors' knowledge, this is the first paper to explore the application of blockchain in the management of EO data provenance.

Graph convolutional neural networks are designed to apply convolutional operations directly on non-Euclidean structure graph data, generating orderly arranged matrix representations of graphs. However, only node features are fully exploited even though edge features may also play an important role in some domains such as chemoinformatics. In this paper, we proposed two new approaches of utilizing edge features on graph convolutional neural networks, Feature embedding adjacent matrix and Reverse graph. Methodologies of basic graph convolutional neural networks only tend to propagate node features to neighbor nodes along edges by convolutional operations. By applying Feature embedding adjacent matrix, edge features are synthesized into node features and also propagated to neighbor nodes during propagation process. Reverse graph approach builds a special auxiliary graph to propagate edge features to neighbor edges. Therefore, a synthetical presentation including both edge features and node features is built. Experiments demonstrated our new approaches improve graph classification accuracies, especially on data sets with low accuracies on basic GCNs.

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property; (2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one ’+’ refers to differential privacy, and the other ’+’ refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.

A simplified approach to accelerate matrix factorization of big data is to parallelize it. A commonly used method is to divide the matrix into multiple non-intersecting blocks and concurrently calculate them. This operation causes the Load balance problem, which significantly impacts parallel performance and is a big concern. A general belief is that the load balance across blocks is impossible by balancing rows and columns separately. We challenge the belief by proposing an approach of “Balanced Partitioning (BaPa)”. We demonstrate under what circumstance independently balancing rows and columns can lead to the balanced intersection of rows and columns, why, and how. We formally prove the feasibility of BaPa by observing the variance of rating numbers across blocks, and empirically validate its soundness by applying it to two standard parallel matrix factorization algorithms, DSGD and CCD++. Besides, we establish a mathematical model of “Imbalance Degree” to explain further why BaPa works well. BaPa is applied to synchronous parallel matrix factorization, but as a general load balance solution, it has significant application potential.

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping; plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots; second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845; and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping; the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft's simple encrypted arithmetic library (SEAL).

This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.

Clinical texts are inherently complex due to the medical domain expertise required for content comprehension. In addition, the unstructured nature of these narratives poses a challenge for automatically extracting information. In natural language processing, the use of word embeddings are an effective approach to generate word representations (vectors) in a low dimensional space. In this paper we use a log-linear model (a type of neural language model) and Linear Discriminant Analysis with a kernel-based Extreme Learning Machine (ELM) to map the clinical texts to the medical code. Experimental results on clinical texts indicate improvement with ELM in comparison to SVM and neural network approaches.

Abstract Cloud computing offers a cheap and efficient solution for the deployment of web applications. It results in a big increase of the number of service provider. Users hold multiple identities for using services from different domains. The openness of public clouds requires the authentication system to accept user identities from various domains and to support hybrid authentication protocols. This work proposes a cross‐domain single sign‐on mechanism to address the preceding issues and makes a formal mathematical model to analyze the security issues of the proposed mechanism's authentication architecture; furthermore, an algorithm is proposed to detect the authentication architecture's weak vertex whose failure would lead to a partial failure in the architecture. The proposed mechanism allows service providers to verify user identities in a decentralized way and allows users to unify their identities from various domains in a safe way. The verification process used in this mechanism is able to support hybrid authentication protocols as well as to accelerate the verification of credentials by eliminating single point of failure and single‐point bottleneck. Copyright © 2015 John Wiley & Sons, Ltd.

For datasets in Collaborative Filtering (CF) recommendations, even if the identifier is deleted and some trivial perturbation operations are applied to ratings before they are released, there are research results claiming that the adversary could discriminate the individual's identity with a little bit of information. In this paper, we propose $k$-coRating, a novel privacy-preserving model, to retain data privacy by replacing some null ratings with "well-predicted" scores. They do not only mask the original ratings such that a $k$-anonymity-like data privacy is preserved, but also enhance the data utility (measured by prediction accuracy in this paper), which shows that the traditional assumption that accuracy and privacy are two goals in conflict is not necessarily correct. We show that the optimal $k$-coRated mapping is an NP-hard problem and design a naive but efficient algorithm to achieve $k$-coRating. All claims are verified by experimental results.

Cloud computing has been acknowledged as one of the prevaling models for providing IT capacities. The off-premises computing paradigm that comes with cloud computing has incurred great concerns on the security of data, especially the integrity and confidentiality of data, as cloud service providers may have complete control on the computing infrastructure that underpins the services. This makes it difficult to share data via cloud providers where data should be confidential to the providers and only authorized users should be allowed to access the data. This work aims to construct a system for trusted data sharing through untrusted cloud providers, to address the above mentioned issue. The constructed system can imperatively impose the access control policies of data owners, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data.