ID-Based Public Auditing Protocol for Cloud Storage Data Integrity Checking with Strengthened Authentication and Security

Cloud storage service reduces the burden of data users by storing users’ data files in the cloud. But, the files might be modified in the cloud. So, data users hope to check data files integrity periodically. In a public auditing protocol, there is a trusted auditor who has certain ability to help users to check the integrity of data files. With the advantages of no public key management and verification, researchers focus on public auditing protocol in ID-based cryptography recently. However, some existing protocols are vulnerable to forgery attack. In this paper, based on ID-based signature technology, by strengthening information authentication and the computing power of the auditor, we propose an ID-based public auditing protocol for cloud data integrity checking. We also prove that the proposed protocol is secure in the random oracle model under the assumption that the Diffie-Hellman problem is hard. Furthermore, we compare the proposed protocol with other two ID-based auditing protocols in security features, communication efficiency and computation cost. The comparisons show that the proposed protocol satisfies more security features with lower computation cost.     

Human Development in Saudi Education: a Critical Systemic Approach

This paper is drawn from PhD research funded by the Saudi government on the policy and governance of Saudi schools (Algraini 2017. A critical systemic approach to human development in education: a case study of the Ministry of Education in Saudi Arabia). The aim of this article is to address human development challenges using the Critical Systems Heuristics approach. The results show that the central policy framework limits the opportunity for teachers and learners to contribute to shaping the curriculum and the process for human development. The Saudi Ministry of Education needs to: a) develop policy that takes into account the structure and the processes that shape education outcomes and b) to represent the voices of service users and service providers who have different and overlapping views.     

A Method to Visualize the Skeleton Industrial Structure with Input-Output Analysis and Its Application in China,Japan and USA

The paper established a double filtering method (DFM) to visualize the skeleton industrial structure (SIS) of one economy and find its evolution rule. Different with the previous researches, this method is from a new view of industrial conjunctions combined by leading sectors to depict the industrial structure. It was proved that the leading sector selected by DFM must be key sector selected by Hirschman-Rasmussen method. Applied DFM to input-output tables of China, Japan and USA and MFA to Japan and USA, the results analysis showed that DFM could overtake the two main shortcomings of minimum flow analysis (MFA), scratch SIS of each economy with its own characteristics, visualize the general evolution rules of the industrial structure with crisscrossed conjunctions among leading sectors.     

Control of excessive neural circuit excitability and prevention of epileptic seizures by endocannabinoid signaling

Progress in research on endocannabinoid signaling has greatly advanced our understanding of how it controls neural circuit excitability in health and disease. In general, endocannabinoid signaling at excitatory synapses suppresses seizures by inhibiting glutamate release. In contrast, endocannabinoid signaling promotes seizures by inhibiting GABA release at inhibitory synapses. The physiological distribution of endocannabinoid signaling molecules becomes disrupted with the development of epileptic focus in patients with mesial temporal lobe epilepsy and in animal models of experimentally induced epilepsy. Augmentation of endocannabinoid signaling can promote the development of epileptic focus at initial stages. However, at later stages, increased endocannabinoid signaling delays it and suppresses spontaneous seizures. Thus, the regulation of endocannabinoid signaling at specific synapses that cause hyperexcitability during particular stages of disease development may be effective for treating epilepsy and epileptogenesis.     

Quantitative Analysis of Multi-Recovery-Based Intrusion Tolerance Model

Quantitative analysis has always been a difficult problem in security analysis of intrusion tolerance systems. An intrusion tolerance model based on multiple recovery mechanisms is introduced in this paper and how to quantify the security attributes of the model is proposed. A state transition model with recovery states more accurately describes the dynamic behavior of the system. Considering that recovery mechanisms have a great impact on the security performance of the system, we set up the cost models corresponding to different recovery mechanisms. We propose a feasible security measure based on mean cost to security failure in order to evaluate the system cost during the recovery phase. The experimental results confirmed the feasibility of the proposed methods.     

The Life-Cycle Model with Optimal Retiring Age and Simulation

In this paper, a life-cycle model with retirement is set up to study how an individual chooses the optimal retiring age on account of wage growth rate, longevity and healthy state. It is proved that there exists optimal retiring age under given conditions. The numerical simulations are given to show how wage growth rate, longevity and healthy state affect retiring age.     

Community Work Research through Co-Operative Inquiry in Timor-Leste,Australia and Peru: Insights into Process

Co-operative inquiry is an under-explored method in community-based research. Its democratic approach involves a group of “co-researchers” collaboratively inquiring into a mutually-agreed research topic through systematic cycles of reflection and action. This article shares the process, outcomes, and methodological reflections of three co-operative inquiries that were conducted as part of an international study regarding love in community work. Groups of up to 10 community workers in rural villages in Timor-Leste, Australia, and Peru engaged in co-operative inquiry to collaboratively develop new knowledge to inform practice. The article analyses data regarding co-inquirer experiences of the methodology, and identifies the strengths and challenges of the co-operative inquiry method to support the collective generation of new knowledge. The article concludes that co-operative inquiry involves shared power, participation, action, and contextual reflexivity, and is a useful method for community workers to collaboratively develop innovative approaches to community work practice through cycles of reflection and action.     

The Convergence of Least-Squares Progressive Iterative Approximation for Singular Least-Squares Fitting System

Data fitting is an extensively employed modeling tool in geometric design. With the advent of the big data era, the data sets to be fitted are made larger and larger, leading to more and more leastsquares fitting systems with singular coefficient matrices. LSPIA (least-squares progressive iterative approximation) is an efficient iterative method for the least-squares fitting. However, the convergence of LSPIA for the singular least-squares fitting systems remains as an open problem. In this paper, the authors showed that LSPIA for the singular least-squares fitting systems is convergent. Moreover, in a special case, LSPIA converges to the Moore-Penrose (M-P) pseudo-inverse solution to the leastsquares fitting result of the data set. This property makes LSPIA, an iterative method with clear geometric meanings, robust in geometric modeling applications. In addition, the authors discussed some implementation detail of LSPIA, and presented an example to validate the convergence of LSPIA for the singular least-squares fitting systems.     

Prediction of end-use energy consumption in a region of Northwest China

End-use energy consumption can reflect the industrial development of a country and the living standards of its residents. The study of end-use energy consumption can provide a solid basis for industrial restructuring, energy saving, and emission reduction. In this paper, we analyzed the end-use energy consumption of a region in Northwestern China, and applied the Markov prediction method to forecast the future demand of different types of end-use energy. This provides a reference for the energy structure optimization in the Northwestern China.