Junsheng Zhang

Data Security and Privacy in Cloud Computing

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of users concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.

Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Massive sensing data are generated continuously in the Internet of Things. How to organize and how to query the big sensing data are big challenges for intelligent applications. This paper studies the organization of big sensing data with event-linked network (ELN) model, where events are regarded as primary units for organizing data and links are used to represent the semantic associations among events. Several different types of queries on the event-linked network are also explored, which are different from queries on traditional relational database. We use an instance of smart home to show the effectiveness and efficiency of organization and query approaches based on the event-linked network.

Schema Theory for Semantic Link Network

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.

Semantic relation computing theory and its application

Semantic relations among objects are primary semantic factors, which play the most important role for human and smart systems /machines to understand and control the situation in the context of connected systems. However, few existing works focus on the study of semantic relations from the mathematical view, though it would be the basis for further research on semantics. Existing research works focus on the representation of semantic relations and reasoning with relations, but seldom concentrate on the semantic relation computing including accurate reasoning, integrity checking and redundancy checking. In this paper, we aim at exploring algebraic computing approach of semantic relations. A mathematical computing theory for establishing semantic relations - semantic relation space theory - is proposed for the first time, including conceptions of semantic relation basis, orthogonal basis, and some basic operations for semantic relations. The proposed theory can be used to represent the semantic relations among objects in an accurate way and to deduce implicit relations in the connected smart systems, especially for automatic reasoning and autonomous computing. Furthermore, integrity and consistency issues among semantic relations are also discussed based on the theory. A case study in scientific research domain has shown the feasibility and effectiveness of the proposed theory.

Superconductivity and magnetism in FeSe thin films grown by metal–organic chemical vapor deposition

High quality FeSe thin films with different ratios of Fe to Se have been grown on GaAs and Si substrates by changing the flow rate of Fe(CO)5 in a low-pressure metal–organic chemical vapor deposition (MOCVD) system. For both substrates, the films grown at a low flow rate of Fe(CO)5 are non-ferromagnetic and exhibit superconductivity at low temperature, while those grown at a high flow rate of Fe(CO)5 display ferromagnetic and semiconducting behaviors. Our results suggest that the superconducting phase does exist in a narrow range of Fe and Se concentration near stoichiometry. The introduction of excess Fe favors ferromagnetism and leads to the suppression of superconductivity.

Automatically constructing semantic link network on documents

Knowing semantic links among resources is the basis of realizing machine intelligence over large‐scale resources. Discovering semantic links among resources with limited human interference is a challenge issue. This paper proposes an approach to automatically discovering and predicting semantic links in a document set based on a model of document semantic link network (SLN). The approach has the following advantages: it supports probabilistic relational reasoning; SLNs and the relevant rules automatically evolve; and, it can adapt to the update of the adopted techniques. The approach can support cyber space applications, such as documentation recommendation and relational queries, on large documents. Copyright

Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Measuring graph similarity is a primary issue for graph-related applications. Many works have been proposed on simple topology-based structural similarity measuring for networks. It is not enough for semantic-rich networks like semantic networks, semantic link networks, and event-linked networks where semantic-based structural similarity measuring is more important than topology-based structure similarity measuring. In this paper, the authors introduce a semantic-based structural similarity for the first time and then propose an approach to measure the semantic-based structural similarity between networks with the computing theory for semantic relations as the foundation. A case study in semantic link network of the scientific research is also presented to show the feasibility of the proposed approach.

Discovering Associations among Semantic Links

Semantic link network is a semantic data model to manage Web resources and semantic relations among them. Its nodes represent Web resources, and its semantic links between the nodes represent the semantic relations between the resources. This paper studies two kinds of associations between semantic link types (relationships): reasoning associations and statistical associations. We propose the approaches to calculating the two kinds of association degrees respectively. Besides, algorithms are developed to discover the statistical association rules. Association between semantic link types are useful in relational query in semantic link networks.

A synergetic mechanism for digital library service in mobile and cloud computing environment

Digital library services in the mobile computing have come into our daily life with the development of wireless communication and the manufacture of mobile devices. Digital libraries have collected large-scaled data for providing information service. However, there are still many problems such as limitations of computing on mobile devices and data transmission problems in the wireless network. This paper proposes a synergetic mechanism for digital library to provide information service in the mobile and cloud computing environment, which combines the digital library servers, mobile hosts and personal cloud space into a synergetic work group seamlessly. The proposed mechanism integrates the strong computing and large-scaled storage ability of cloud computing together with the convenience of mobile computing. It could help promoting knowledge sharing and improve work efficiency of users in the mobile and cloud computing environment.

Automatically Discovering Semantic Links among Documents

Knowing semantic links among documents is the basis for intelligent applications over large-scale document resources. Discovering these semantic links with little human interference is a challenge issue. This paper proposes an approach to automatically discover semantic links in document set based on a probabilistic documentary semantic link network model. The approach has the following advantages: 1) It supports probabilistic relational reasoning. 2) The semantic link networks and relevant rules automatically evolve. 3) It does not rely on any predefined ontology. 4) It can adapt to the update of the adopted techniques. Experiments on document sets of different types (scientific papers and Web pages) and different scales show the proposed approach feasible. The approach can be used to automatically construct semantic overlays on large document sets to support advanced applications like various relational queries on scientific documents.