Design and implementation of a confidentiality and access control solution for publish/subscribe systems

The publish/subscribe model offers a loosely-coupled communication paradigm where applications interact indirectly and asynchronously. Publishers generate events that are sent to interested applications through a network of brokers. Subscribers express their interest by specifying filters that brokers can use for routing the events. Supporting confidentiality of messages being exchanged is still challenging. First of all, it is desirable that any scheme used for protecting the confidentiality of both the events and filters should not require publishers and subscribers to share secret keys. In fact, such a restriction is against the loose-coupling of the model. Moreover, such a scheme should not restrict the expressiveness of filters and should allow the broker to perform event filtering to route the events to the interested parties. Existing solutions do not fully address these issues. In this paper, we provide a novel scheme that supports (i) confidentiality for events and filters; (ii) allows publishers to express further constraints about who can access their events; (iii) filters that can express very complex constraints on events even if brokers are not able to access any information in clear on both events and filters; (iv) and, finally, it does not require publishers and subscribers to share keys. Furthermore, we show how we applied our scheme to a real-world e-health scenario, developed together with a hospital. We also describe the implementation of our solution in Java and the integration with an existing publish/subscribe system.     

基于检索决策树的螨种分类鉴定专家系统

应用传统的分类检索方法进行粉螨亚目螨种的分类鉴定是一个复杂、繁琐的过程。随着人工智能技术的发展,采用专家系统技术编制螨种分类鉴定软件将改变传统的分类鉴定方式。通过学习粉螨亚目的分类学和专家系统的相关知识,将螨种的分类鉴定与专家系统有机地结合起来,采用面向对象方法,设计并实现了基于决策树的螨种分类鉴定专家系统。通过构建基于面向对象的决策树知识表示模型,提炼出螨种知识库的记录结构,根据节点对象的逻辑关系,设计了基于决策树的推理机制,并按照一定的推理控制策略实现了螨种的分类鉴定。结果证明,满足了用户的需要,具有重要的实践价值。     

Alcohol consumption detection through behavioural analysis using intelligent systems

We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.     

PVA-based activated carbon fibers with lotus root-like axially porous structure

A novel method was developed for the fabrication of activated carbon fiber (ACF) with wet spinning polyvinyl alcohol (PVA) fibers as the precursors. Through a combination of preoxidation, dehydration, carbonization and activation under a certain tension, PVA-based ACFs (PVA-ACFs) with high yields and good mechanical properties were obtained. The surface and cross-section morphologies, pore structures, surface geometries, surface functional groups and crystal structures of the PVA-ACFs were characterized using field emission scanning electron microscopy, low temperature nitrogen adsorption, Fourier transformed infrared spectrophotometry, X-ray photoelectron spectroscopy and X-ray power diffraction. All the PVA-ACF samples prepared had lotus root-like axially meso- and macroporous structures with the domination of micropores. Fractal geometries of PVA-ACFs deduced from nitrogen adsorption isotherms indicates that capillary force dominated in the interactions between nitrogen and the PVA-ACFs. The surface functional groups of the PVA-ACFs depend on the activating agents. Graphitoidal crystal structures were observed for the PVA-ACFs. The small crystal size and short range ordering between the crystallites ensured a high specific surface area of the PVA-ACFs.     

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.     

A general transformation from KP-ABE to searchable encryption

Users are inclined to share sensitive data in a remote server if no strong security mechanism is in place. Searchable encryption satisfies the need of users to execute a search encrypted data. Previous searchable encryption methods such as “public key encryption with keyword search (PEKS)” restricted the data access to certain users, because only the assigned users were able to search the encrypted data. In this paper we will discuss the relation between Attribute Based Encryption (ABE) and searchable encryption and define a weak anonymity of the ABE scheme, named “attribute privacy”. With this weak anonymity, we propose a general transformation from ABE to Attribute Based Encryption with Keyword Search (ABEKS) and a concrete attribute private key-policy ABE (KP-ABE) scheme. We present an ABEKS scheme based on this KP-ABE scheme and permit multi-users to execute a flexible search on the remote encrypted data.     

F离子掺杂TiO2的性能研究

以NH4F为掺杂剂，采用溶胶-凝胶法制备F离子掺杂型TiO2光催化剂，对其进行XRD、XPS和PL表征，结果表明，F离子掺杂ToO2由于Ti—F配位体的形成而能抑制金红石相的生成，同时F离子掺杂能增加TiO2表面缺陷浓度并降低Ti2P键的结合能，另外，由于F离子能取代Ti—OH配位体而降低了表面羟基氧浓度．光催化研究结果表明，F离子掺杂提高了TiO2光催化活性近1．5倍．     

Discovering small-world in association link networks for association learning

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of “learning from Web resources”). First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners.     

When smart grid meets PHEVs: a smart load distribution mechanism in smart grid

Along with the increasing popularity of electric vehicles caused by economic and environmental incentives, the penetration of plug-in hybrid electric vehicles (PHEVs) poses a great threat to the power grid, especially to the aggregated load in the power system. Motivated by this observation, in this paper, we analyze the impact of large-scale usage of PHEVs and address the load distribution problem by solving a decentralized optimization problem and smoothening the peak load with pricing strategies in the power grid. We also investigate the influence of charging time and charging mode on load distribution, as the charging price varies with the changes of these two factors. Our simulation study on PJM’s data warehouse shows that the proposed strategies can well smooth the peak load by pricing on the charging time and mode. The results also indicate that our strategies always distribute the load in a smoother manner at a smaller load fluctuation compared with other schemes (e.g., First Come First Service and PMCS) and thus improving the stability and reliability of the power grid.