Journal of Ambient Intelligence and Humanized Computing

Collaborative cloud business models enable a new dimension of business by giving option to the third party software vendors to deploy their software in the cloud for offering software as a service (SaaS) to the users. However, the secure provisioning of resources requires scalable architecture with efficient authentication for configuring the collaborative software services in the cloud. In this paper, we propose a novel hierarchical certificateless aggregate signature to provide a scalable authentication model for SaaS in cloud computing. Our proposed scheme is secure under the adaptive chosen-message attack in the random oracle model with the hardness assumption of Computational Diffie–Hellman (CDH) problem and Decisional Diffie–Hellman (DDH) problem. Furthermore, our proposed scheme is highly efficient regarding low overhead on computation and communication cost.

Suppressing noise while preserving textures is one of the most important and challenging problems in natural image denoising. Various priors of natural image, such as gradient based prior, nonlocal self-similarity based prior etc., have been widely studied for noise removal. The methods based on these priors may smooth the fine scale image textures and degrade visual quality of the image. To improve image visual quality, an improved texture-preserved total variation (TPTV) image denoising model with an adaptive fidelity item is proposed in this paper. Firstly, we construct an image structure control function (SCF) based on structure tensor to describe the image structure information. Secondly, we combine SCF into a total variation framework for noise removal such that the model can adaptively balance its regular and fidelity item to keep fine scale features while denoising. Finally, extensive experimental evaluations demonstrate that our TPTV model can well preserve the texture appearance in the denoised image and make them more natural. Besides, it overcomes staircase and over-smoothing effects compared with some competing algorithms.

In the original publication, first author WanJun Yin’s affiliation was incorrectly added.

Various e-learning environments have been developed to provide sufficient materials for learners and thereby guide them to gain knowledge in any domain. Even though there were several factors that determine the motivation of the student, the skill set possessed by them is definitely an important factor. Hence in our proposed work, the behavioural and educational skill of the learners is tested by a skill test and learning content is provided only based on their skill test evaluation reports. The entire process is done by real time personalization based Adaptive Learning Management System and Personalized Page Rank algorithm. Finally, the Navies’ Bayes classifier was employed to classify the learners based on the performance over the skill test. Learners on their own can express both the optimistic and adverse skills that considerably impact the adaptive learning in e-learning environment. High skilled categorized learners are offered with advanced, intermediate learners with moderate and beginner or slow learners are provided with basic level of study content. The results are analysed based on their skills, individual's method of learning and time constraints.

Diagnosability is an important characteristic indicator to determine whether the system is stable and reliable. In this paper, the general architecture of event-state-combination diagnosability is investigated. The contributions are threefold. First, the notion of event-state-combination diagnosability is formalized. Roughly speaking, an event-state-combination diagnosable system means that not only each combined fault can be detected, but also the system can determine whether it will work permanently in the failure states after the combined fault occurs. Then, an automaton with new information structure, called event-state-combination verifier, is constructed, which can be used for the verification of the event-state-combination diagnosability. Finally, the necessary and sufficient conditions for verifying whether the system is event-state-combination diagnosable is presented, that is, the event-state-combination verifier does not have any failure confused cycle.

Human beings tend to express various emotions based on the activities. The criticality of facial expression has been recognized widely in the social interaction along with the social intellect. Human perception is subjective in nature and this makes the classification of emotion an extremely challenging problem. The mood, personality, age, and environment have a major influence on the perception of emotion. Facial expressions are a key to emotion; various studies devote on emotion classification based on facial expression. For the identification of these emotions, there is a mixture of models that make use of the feature representation that are gradient based, a mixture of various dynamic textures along with contextual information. In this work, histogram of optical flow (HOF) was used for the extraction of features and a neural network for bringing about an improvement to the accuracy of classification. With the availability of big data analytics, there has been a major increase in the power of computation in terms of analysing live video data, huge number of images and faster processing which is critical for emotion classification. The work has investigated efficacy of the flow of HOF and proposed a modified fuzzy histogram of optical flow. For choosing optimal rules in fuzzy system, heuristic method namely, charged system search was used. The results have proved that there has been a significant improvement to the methodology proposed.

Most advantages that the smart grid will bring derive from its capability of improving reliability performance and customers’ responsiveness and encouraging greater efficiency decisions by the costumers. Demand side management is, therefore, considered as an integral part of the smart grid and one of the most important methods of energy saving. Accordingly, an innovative decision support and energy management system (DSEMS) for residential applications is proposed in this paper. The DSEMS is represented as a finite state machine and consists of a series of scenarios that may be selected according to the user preferences. The designing and testing methods are described and some simulations results are presented in order to verify its effectiveness both in terms of continuity of electricity supply and energy savings and economics.

Data transmission over wireless networks become increasing the energy levels which has the attention of both the routing data and the better communication in network. Due to lacking of improper network signals and the effluence it causes to the energy consumption in wireless network. With the progressive advancement in CDMA based radio communication channels are used to make better communication. This allow the multicasting techniques covers through transmitters to send he data in a single communication channel. To propose a Multi linear adaptive sequence transmission (MAST-CDMA) used to sense the channels for sequence linearity to transfer the data to make decisions without traffic constraints on periodic times. The routing impacts of packets over the network transmitters are based on the Multicast neighbor data communication (MNDC).The adaptive technique reduce the energy consumption over delay occurred on bandwidth consideration through transmitter in single channel communication. The newly communication intent are neighbor routing which is examined by traffic flow analyze the transmission consumption to improve the energy level constraints to produce improved network efficiency.

The increased demand for information systems drives businesses to rethink their customer needs to a greater extent and undertake innovation to compete in the marketplace. The design thinking (DT) is a human-centered methodology leads to creativity and innovation. The agile applications development such as extreme programming (XP) as a rapid application development approach tends to focus on perfecting functionality requirement and technical implementation. However, it causes significant challenges to building software/applications to meet the needs of end-user. This study integrates DT practices into XP methodology to improve the quality of software product for the end-users and enable businesses to achieve creativity and innovation. The proposed integrated DT@XP framework presents the various DT practices (empathy, define, persona, DT user stories) are adapted into XP exploration phase, prototyping and usability evaluation into XP planning phase. Our work demonstrates the applicability of DT concepts to analyze customer/user involvement in XP projects.