Dual compressible hybrid quantum secret sharing schemes based on extended unitary operations

A crucial issue with hybrid quantum secret sharing schemes is the amount of data that is allocated to the participants. The smaller the amount of allocated data, the better the performance of a scheme. Moreover, quantum data is very hard and expensive to deal with, therefore, it is desirable to use as little quantum data as possible. To achieve this goal, we first construct extended unitary operations by the tensor product of n, n ≥ 2, basic unitary operations, and then by using those extended operations, we design two quantum secret sharing schemes. The resulting dual compressible hybrid quantum secret sharing schemes, in which classical data play a complementary role to quantum data, range from threshold to access structure. Compared with the existing hybrid quantum secret sharing schemes, our proposed schemes not only reduce the number of quantum participants, but also the number of particles and the size of classical shares. To be exact, the number of particles that are used to carry quantum data is reduced to 1 while the size of classical secret shares also is also reduced to l−2 m−1 based on ((m+1, n′)) threshold and to l−2 r2 (where r2 is the number of maximal unqualified sets) based on adversary structure. Consequently, our proposed schemes can greatly reduce the cost and difficulty of generating and storing EPR pairs and lower the risk of transmitting encoded particles.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

Noise Filtering of Process Execution Logs based on Outliers Detection

This paper presents a technique for the automated removal of noise from process execution logs. Noise is the result of data quality issues such as logging errors and manifests itself in the form of infrequent process behavior. The proposed technique generates an abstract representation of an event log as an automaton capturing the direct follows relations between event labels. This automaton is then pruned from arcs with low relative frequency and used to remove from the log those events not fitting the automaton, which are identified as outliers. The technique has been extensively evaluated on top of various auto- mated process discovery algorithms using both artificial logs with different levels of noise, as well as a variety of real-life logs. The results show that the technique significantly improves the quality of the discovered process model along fitness, appropriateness and simplicity, without negative effects on generalization. Further, the technique scales well to large and complex logs.

Developing an epidemic forecasting model for Influenza A in Brisbane, Australia, based on climate and Hong Kong Influenza A surveillance data

Influenza is associated with substantial disease burden [ 1]. Development of a climate-based early warning system for in fluenza epidemics has been recommended given the signi fi - cant association between climate variability and influenza activity [2]. Brisbane is a subtropical city in Australia and offers free in fluenza vaccines to residents aged ≥65 years considering their high risks in developing life-threatening complications, especially for in fluenza A predominant seasons. Hong Kong is an international subtropical city in Eastern Asia and plays a crucial role in global infectious diseases transmission dynamics via the international air transportation network [3, 4]. We hypothesized that Hong Kong in fluenza surveillance data could provide a signal for in fluenza epidemics in Brisbane [ 4]. This study aims to develop an epidemic forecasting model for influenza A in Brisbane elders, by combining climate variability and Hong Kong in fluenza A surveillance data. Weekly numbers of laboratoryconfirmed influenza A positive isolates for people aged ≥65 years from 2004 to 2009 were obtained for Brisbane from Queensland Health, Australia, and for Hong Kong from Queen Mary Hospital (QMH). QMH is the largest public hospital located in Hong Kong Island, and in fluenza surveillance data from this hospital have been demonstrated to be representative for influenza circulation in the entirety of Hong Kong [ 5]. The Brisbane in fluenza A epidemics occurred during July –September, whereas the Hong Kong in fluenza A epidemics occurred during February –March and May –August.

Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

Improving adherence physical activity with a smartphone application based on adults with intellectual disabilities (APPCOID)

Background People with intellectual disabilities (ID) have lower levels of physical activity and quality of life and they have a lot of barriers to face when taking part in physical activity. Other problems are the poor adherence to physical activity such people have so this study is designed to improve adherence to physical activity for people with intellectual disabilities with the assistance of an application for smartphones. The aim of the study will be to improve physical activity and physical condition after multimodal intervention and to analyse the promotion of adherence to physical activity through a multimodal intervention and an app intervention (mHealth) in people with ID. Methods A two-stage study will be conducted. In stage 1 a multimodal intervention will take place will be done with physical activity and educational advice over eight weeks, two days a week. Data will be measured after and before the intervention. In stage 2 a randomized controlled trial will be conducted. In the intervention group we will install an application to a smartphone; this application will be a reminder to do a physical activity and they have to select whether they have or haven’t done a physical activity every day. This application will be installed for 18 weeks. Data will be measured after and before the application is installed in two groups. We will measure results 10 weeks later when the two groups don’t have the reminder. The principal outcome used to measure the adherence to physical activity will be the International Physical Activity Questionnaire; secondary outcomes will be a fun-fitness test and self-report survey about quality of life, self-efficacy and social support. Samples will be randomized by sealed envelope in two groups, with approximately 20 subjects in each group. It’s important to know that the therapist will be blinded and won’t know the subjects of each group. Discussion Offering people with ID a multimodal intervention and tool to increase the adherence to a physical activity may increase the levels of physical activity and quality of life. Such a scheme, if beneficial, could be implemented successfully within public health sense. Trial registration ClinicalTrials.gov Identifier: NCT01915381.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

A novel measure of fractional anisotropy based on the tensor distribution function

Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Perceived quality of e‐health services: A conceptual scale development of e‐health service quality based on the C‐OAR‐SE approach

Purpose Health service quality is an important determinant for health service satisfaction and behavioral intentions. The purpose of this paper is to investigate requirements of e‐health services and to develop a measurement model to analyze the construct of “perceived e‐health service quality.” Design/methodology/approach The paper adapts the C‐OAR‐SE procedure for scale development by Rossiter. The focal aspect is the “physician‐patient relationship” which forms the core dyad in the healthcare service provision. Several in‐depth interviews were conducted in Switzerland; first with six patients (as raters), followed by two experts of the healthcare system (as judges). Based on the results and an extensive literature research, the classification of object and attributes is developed for this model. Findings The construct e‐health service quality can be described as an abstract formative object and is operationalized with 13 items: accessibility, competence, information, usability/user friendliness, security, system integration, trust, individualization, empathy, ethical conduct, degree of performance, reliability, and ability to respond. Research limitations/implications Limitations include the number of interviews with patients and experts as well as critical issues associated with C‐OAR‐SE. More empirical research is needed to confirm the quality indicators of e‐health services. Practical implications Health care providers can utilize the results for the evaluation of their service quality. Practitioners can use the hierarchical structure to measure service quality at different levels. The model provides a diagnostic tool to identify poor and/or excellent performance with regard to the e‐service delivery. Originality/value The paper contributes to knowledge with regard to the measurement of e‐health quality and improves the understanding of how customers evaluate the quality of e‐health services.

Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses

Monitoring pedestrian and cyclists movement is an important area of research in transport, crowd safety, urban design and human behaviour assessment areas. Media Access Control (MAC) address data has been recently used as potential information for extracting features from people’s movement. MAC addresses are unique identifiers of WiFi and Bluetooth wireless technologies in smart electronics devices such as mobile phones, laptops and tablets. The unique number of each WiFi and Bluetooth MAC address can be captured and stored by MAC address scanners. MAC addresses data in fact allows for unannounced, non-participatory, and tracking of people. The use of MAC data for tracking people has been focused recently for applying in mass events, shopping centres, airports, train stations etc. In terms of travel time estimation, setting up a scanner with a big value of antenna’s gain is usually recommended for highways and main roads to track vehicle’s movements, whereas big gains can have some drawbacks in case of pedestrian and cyclists. Pedestrian and cyclists mainly move in built distinctions and city pathways where there is significant noises from other fixed WiFi and Bluetooth. Big antenna’s gains will cover wide areas that results in scanning more samples from pedestrians and cyclists’ MAC device. However, anomalies (such fixed devices) may be captured that increase the complexity and processing time of data analysis. On the other hand, small gain antennas will have lesser anomalies in the data but at the cost of lower overall sample size of pedestrian and cyclist’s data. This paper studies the effect of antenna characteristics on MAC address data in terms of travel-time estimation for pedestrians and cyclists. The results of the empirical case study compare the effects of small and big antenna gains in order to suggest optimal set up for increasing the accuracy of pedestrians and cyclists’ travel-time estimation.

Bias-dependent effects in planar perovskite solar cells based on CH3NH3PbI3−xClx films

A unique bias-dependent phenomenon in CH3NH3PbI3−xClx based planar perovskite solar cells has been demonstrated, in which the photovoltaic parameters derived from the current–voltage (I–V) curves are highly dependent on the initial positive bias of the I–V measurement. In FTO/CH3NH3PbI3−xClx/Au devices, the open-circuit voltage and short-circuit current increased by ca. 337.5% and 281.9% respectively, by simply increasing the initial bias from 0.5 V to 2.5 V.

An efficient hole transport material composite based on poly(3-hexylthiophene) and bamboo-structured carbon nanotubes for high performance perovskite solar cells

In this work, we have developed a new efficient hole transport material (HTM) composite based on poly(3- hexylthiophene) (P3HT) and bamboo-structured carbon nanotubes (BCNs) for CH3NH3PbI3 (MAPbI3) based perovskite solar cells. Compared to pristine P3HT, it is found that the crystallinity of P3HT was significantly improved by addition of BCNs, which led to over one order of magnitude higher conductivity for the composite containing 1–2 wt% BCNs in P3HT. In the meantime, the interfacial charge transfer between the MAPbI3 light absorbing layer and the HTM composite layer based on P3HT/BCNs was two-fold faster than pristine P3HT. More importantly, the HTM film with a superior morphological structure consisting of closely compact large grains was achieved with the composite containing 1 wt% BCNs in P3HT. The study by electrochemical impedance spectroscopy has confirmed that the electron recombination in the solar cells was reduced nearly ten-fold with the addition of 1 wt% carbon nanotubes in the HTM composite. Owing to the superior HTM film morphology and the significantly reduced charge recombination, the energy conversion efficiency of the perovskite solar cells increased from 3.6% for pristine P3HT to 8.3% for P3HT/(1 wt% BCNs) with a significantly enhanced open circuit voltage (Voc) and fill factor (FF). The findings of this work are important for development of new HTM for high performance perovskite solar cells.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.