The Internet of Things (IoT) is vulnerable to leakage of private information during data sharing. To avoid this problem, access control and secure data sharing have been introduced in IoT; however, many challenges are faced because of centralized access control and single delegator selection. Additionally, blockchain is integrated into IoT to enhance the security of the environment. For that purpose, this research proposes dynamic secure access control using the blockchain (DSA-Block) model, which performs secure access control and data sharing. Initially, the IoT device attributes and user attributes are registered at a local domain authority (LDA) for generating private and public keys using the hyperelliptic curve cryptography (HECC) algorithm, which ensures the legitimacy of the users and devices. Then, the IoT devices send a request message to the edge nodes (ENs) via a gateway, which performs request filtration by validating the user’s authenticity. The filtered requests are sent to the edge server to perform access delegation using rock hyraxes swarm optimization (RHSO), which selects a set of delegator nodes. The access control decision is made by using the Trusted practical Byzantine fault tolerance (PBFT) consensus algorithm. The IoT data are stored in the cloud server for secure storage, in which the data are secured using a differential privacy mechanism. Finally, dual revocations, such as user attribute revocation and user revocation, are used to maintain security. The performance of DSA-Block is evaluated and the results demonstrate that the proposed DSA-Block model achieves superior performance compared to previous works.

Recently, robot arms have become an irreplaceable production tool, which play an important role in the industrial production. It is necessary to ensure the absolute positioning accuracy of the robot to realize automatic production. Due to the influence of machining tolerance, assembly tolerance, the robot positioning accuracy is poor. Therefore, in order to enable the precise operation of the robot, it is necessary to calibrate the robotic kinematic parameters. The least square method and Levenberg-Marquardt (LM) algorithm are commonly used to identify the positioning error of robot. However, it generally has the overfitting caused by improper regularization schemes. To solve this problem, this article discusses six regularization schemes based on its error models, i.e.,$L_{1}$,$L_{2}$, dropout, elastic, log, and swish. Moreover, this article proposes a scheme with six regularization to obtain a reliable ensemble, which can effectively avoid overfitting. The positioning accuracy of the robot is improved significantly after calibration by enough experiments, which verifies the feasibility of the proposed method.

This paper describes our approach to build a dynamic adaptive course in learning management systems based on students' learning styles. Our method aims to adapt the student's learning style by taking the advantage of the previous students taught the same course. To achieve this goal, we present an approach for adaptivity by taking the advantage of information that can be taken from previous students' data record to find similarity between the students. Based on this similarity we can find the next suitable learning style for the current students.

Learning management systems are widely and successfully used for the delivery of self-paced online courses. This paper is concerned with enhancing the capability of such systems by introducing adaptivity in the way the information is presented to the on-line learner. Adaptivity means that the presentation style is personalised to the preferred learning style of each student, as may be modelled by a Bayesian Network. The modelling is based on learning theories that have been proven by much experience, and provide appropriate assessment procedures. Techniques for introducing two forms of adaptivity, static and dynamic are considered and two experiments, based on case-studies are devised for evaluating their potential.

We present a novel agent-based threshold proxy signcryption (ATPS) protocol, which enables an agent owner to securely delegate his signing capabilities to a mobile agent so that the mobile agent can sign documents on behalf of its owner. The protocol incorporates three novel contributions: a proxy key generation method, a threshold proxy signcryption scheme and a proxy signature verification method. The most notable feature of the protocol is that it enables a mobile agent to securely, efficiently and autonomously sign documents on the owner's behalf in a remote host with nonrepudiation and fairness assurance for both the owner and the remote host. The security of the proposed protocol is analyzed and its performance against other related work is evaluated.