A study on effects of security risks on acceptance of enterprise cloud service: moderating of employment and non-employment using PLS multiple group analysis

This paper analyzed the effects of security risk factors fit for cloud computing paradigm on the acceptance of enterprise cloud service with intent to illuminate the factors for vitalizing the adoption of corporate cloud services in the future. The acceptance intention was set as a dependent variable. Independent variables were set in reference to the technology acceptance theory. Security risks were categorized into compliance risk, information leakage risk, troubleshooting risk and service discontinuation risk to design a model for analysis.     

Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Theoretical justification of approximate norm minimization method for intelligent digital redesign

This paper investigates the theoretical justification of intelligent digital redesign (IDR) methods based on the approximate discrete-time models. Its main purpose is to design the digital fuzzy controller achieving the minimum norm distance between the trajectories of the approximate closed-loop systems under the analog and the digital controls. It is verified that, if there exist the digital gains such that the approximate norm distance is sufficiently small, the redesigned controller makes the trajectories of the exact closed-loop systems close, or, at least, asymptotically stabilizes the plant.     

Bio-Inspired Artificial Fast-Adaptive and Slow-Adaptive Mechanoreceptors With Synapse-Like Functions

Development of artificial mechanoreceptors capable of sensing and pre-processing external mechanical stimuli is a crucial step toward constructing neuromorphic perception systems that can learn and store information. Here, bio-inspired artificial fast-adaptive (FA) and slow-adaptive (SA) mechanoreceptors with synapse-like functions are demonstrated for tactile perception. These mechanoreceptors integrate self-powered piezoelectric pressure sensors with synaptic electrolyte-gated field-effect transistors (EGFETs) featuring a reduced graphene oxide channel. The FA pressure sensor is based on a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) thin film, while the SA pressure sensor is enabled by a piezoelectric ionogel with the piezoelectric-ionic coupling effect based on P(VDF-TrFE) and an ionic liquid. Changes in post-synaptic current are achieved through the synaptic effect of the EGFET by regulating the amplitude, number, duration, and frequency of tactile stimuli (pre-synaptic pulses). These devices have great potential to serve as artificial biological mechanoreceptors for future artificial neuromorphic perception systems.     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems

This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.