Miracle-dz/UniFAR-FTUs huggingface dataset:
MARINS: A Mobile Smartphone AR System for Pathfinding in a Dark Environment

Traditional egress routes are normally indicated on floor plans and function as designed, assuming that people can identify their relative location and orientation. However, the evacuation process can easily become complicated in a dark or hazardous environment with potential blockage of unexpected obstacles. This study developed the mobile AR indoor navigation system (MARINS) using a smartphone as a device to guide users to exits in a 0-lux setting with the path only illuminated by the phone camera’s LED. The system is developed using Apple ARKit SDK with the associated simultaneous localization and mapping (SLAM) function on a Unity platform in four modules. A maze scenario is planned in an environment built by carton walls. Time and distance traveled by the experimental group and the control group are measured. The results of statistical analysis demonstrate that the MARINS system can reduce travel time in known space and in total summation compared to the application of a traditional map. The system also reduces travel distance and misjudgments with higher system usability than the application of a traditional map.
Multi-user Input in Determining Answer Sets (MIDAS)

Empirical validation is an important component of sound requirements engineering research. Many researchers develop a gold standard or answer set against which to compare techniques that they also developed in order to calculate common measures such as recall and precision. This poses threats to validity as the researchers developed the gold standard and the technique to be measured against it. To help address this and to help reduce bias, we introduce a prototype of Multi-user Input in Determining Answer Sets (MIDAS), a web-based tool to permit communities of researchers to jointly determine the gold standard for a given research data set. To date, the tool permits community members to add items to the answer set, vote on items in the answer set, comment on items, and view the latest status of community opinion on answer set items. It currently supports traceability data sets and classification data sets.
Crowd Intelligence for Decision Making Based on Positive and Negative Comparing With Linguistic Scale

Crowd intelligence opens up new ways for decision making in open environments, traditional decision making is unable to effectively make correct decisions in open environments. In this paper, positive and negative comparing method using linguistic scale is proposed to make decisions in the open environments with crowd intelligence. Firstly, the crowd participants compare the alternative with the corresponding positive and negative assessment points, and give their evaluations using linguistic scales form positive and negative views. The crowd participants’ evaluations can be translated into Intuitionistic Fuzzy Numbers (IFNs). In the proposed methods, the evaluations given by the crowd participants do not depend on the pairwise comparisons of the alternatives, the consistent problem can be avoided. Secondly, the consensus measures between aggregating results and IFNs are proposed. Based on these concepts, the aggregating methods that without discarding any IFNs are proposed and studied. The studying results show that the proposed methods can improve the consensus measures between the aggregating result and evaluations given by crowd participants.
Fuzzy Aggregation for Rule Selection in Imbalanced Datasets Classification using Choquet Integral

Handling imbalanced datasets is a challenging problem in Knowledge Discovery in Databases. Several associative classification approaches have been proposed in order to treat this kind of data. However, these approaches suffer from a major drawback which is the use of a unique single measure for the filtering and selection of rules. This could be misleading since each measure is constructed in order to select a specific category of rules. To overcome such drawback, we introduce, IARCID, a novel approach that consists in aggregating several measures during the rule selection phase using the fuzzy Choquet Integral in order to produce a global measure. This latter is shown to be suitable for the classification of Imbalanced datasets from different domains. The performance of IARCID is assessed on four datasets with reference to three evaluation metrics. Experimentations show that IARCID outperforms approaches which use one single measure for the rule selection, by offering good classification results with all the datasets and according to all the assessment measures used for.
Scalable Massive Random Access in C- RAN with Fronthaul Limitations

Massive connectivity for massive machine-type communications (mMTC) is key enabler for many 5G and Intternet-of-Things (IoT) applications. In mMTC scenarios, a large number of low-complexity, low-rate devices transmit sporadically over shared scarce communication resources. Reliability and low-latency with short packet transmissions are major design criteria that require integration of novel network architecture solutions and multiple access methods. This study addresses the challenge of massive connectivity in the context of a Cloud Radio Access Network (C-RAN), characterized by a hierarchical structure in which part of the processing functionalities of the Radio Units (RUs) are migrated to a centralized cloud processor or Central Unit (CU). A major architectural constraint of C-RANs is imposed by capacity-limited fronthaul links connecting the RUs with the CU. In this study, the performance of a transmission scheme for massive connectivity is investigated under this architectural constraint. In particular, a non-coherent, grant-free transmission scheme is proposed where the encoding is performed based on a Gabor frame structure. The fronthaul processing is a particular instance of Detect-and-Forward (DtF), where local detection at the RUs is performed using a one-step thresholding procedure, and the local estimates are then merged at the central processor via the capacity-limited fronthaul links. Numerical results illustrate the potential of the proposed scheme to support massive, reliable random access with short messages.
Spike Train Synchrony Analysis of Neuronal Cultures

Spike train synchrony estimation of neuronal cultures provides valuable insights into firing patterns of neurons in terms of degree of similarity or dissimilarity. These estimations have proven to be a useful tool in neuroscience since synchrony in neuronal networks is thought to be related to cognitive processes, sensory awareness, learning and neurological disorders. Many mathematical measures have been developed to quantify the degree of synchrony. These synchrony metrics are generally used for smaller sets of spike trains and not been explored for larger High Density Multi Electrode Arrays (HD-MEA) datasets with thousands of channels. Here, bivariate and multivariate ISI-distance and SPIKE-distance metrics are utilized on both synthetic and experimental HD-MEA datasets to quantify spike train synchrony. It is demonstrated that, despite the significant size of the datasets, the approaches are effective in identifying and quantifying interesting bursting or change in spike train behaviours which are not always obvious from the raster plot.
Contact Lens Detection using Transfer Learning with Deep Representations

Iris scans are most promising, secure and stable than any other biometrics such as fingerprints etc. However, the Iris Recognition Systems (IRS) are exposed to the possibility of being attacked by spoofing using contact lenses. Consequently, detecting the presence of contact lenses becomes obligatory for an IRS to work meticulously. The main contribution of this paper is to present a transfer learning technique relying on a pre-trained deep Convolutional Neural Network (CNN) to extract features followed by principal component analysis (PCA) based feature selection. At last, a cubic Support Vector Machine (cSVM) is used for training Error-Correcting Output Code (ECOC) multiclass model to detect the presence and type of a contact lens. The pre-learnt CNN architecture used here is trained on a huge size of dataset which can be transferred to contact lens detection with a smaller sized dataset. The proposed method is evaluated on two publicly available databases namely, IIIT-D and ND in order to ascertain the adaptability of our method. The comparative analysis affirms the performance superiority (i.e., correct classification rate (CCR)) of the proposed method over the state-of-the-art contact lens detection algorithms.
Design of the IBM z14 microprocessor

The latest-generation IBM Z processor provides enhanced performance and compute capacity compared to its IBM z13 predecessor. This paper describes some of the major improvements that include an additional perceptron branch predictor, a completely redesigned translation engine that is tightly integrated into the core pipeline, and an integrated level-1 cache directory and translation lookaside buffer design. Outside of the central processing unit (CPU), the cache sizes have increased on each cache level, and each processor chip now contains 10 CPUs. The system topology has been optimized to improve cache transfer latencies for workloads spanning multiple processor chips. The bus interfaces between the chips have been redesigned to improve peak bus traffic handling. In combination, these enhancements provide significant performance improvements in traditional data-serving workloads, as well as in virtualized Linux environments running database, analytic, and cognitive workloads.
Turbulence generation from a stochastic wavelet model

This research presents a new turbulence generation method based on stochastic wavelets and tests various properties of the generated turbulence field in both the homogeneous and inhomogeneous cases. Numerical results indicate that turbulence fields can be generated with much smaller bases in comparison to synthetic Fourier methods while maintaining comparable accuracy. Adaptive generation of inhomogeneous turbulence is achieved by a scale reduction algorithm, which greatly reduces the computation cost and practically introduces no error. The generating formula issued in this research could be adjusted to generate fully inhomogeneous and anisotropic turbulence with given RANS data under divergence-free constraint, which was not achieved previously in similar research. Numerical examples shows that the generated homogeneous and inhomogeneous turbulence are in good agreement with the input data and theoretical results.
Vestibulo-Hippocampal Function Is Enhanced and Brain Structure Altered in Professional Ballet Dancers

Background and Objective: Life-long balance training has been shown to affect brain structure, including the hippocampus. Data are missing in this respect on professional ballet dancers of both genders. It is also unknown whether transfer effects exist on general balancing as well as spatial orientation abilities, a function mainly supported by the hippocampus. We aimed to assess differences in gray matter (GM) structure, general balancing skills, and spatial orientation skills between professional ballet dancers and non-dancers. Methods: Nineteen professional ballet dancers aged 18–35 (27.5 ± 4.1 years; 10 females) and nineteen age-matched non-dancers (26.5 ± 2.1 years; 10 females) were investigated. Main outcomes assessed were the score of a 30-item clinical balance test (CBT), the average error distance (in centimeters) on triangle completion task, and difference in GM density as seen by voxel-based morphometric analysis (VBM, SPM). Results: Ballet group performed significantly better on all conditions of the CBT and in the wheelchair (vestibular-dependent) condition of the spatial orientation test. Larger GM volumes for ballet dancers were observed in the right hippocampus, parahippocampal gyrus, insula, and cingulate motor cortex, whereas both larger and smaller volumes were detected within cerebellum bilaterally in comparison to non-dancers. Conclusion: Our results indicate that life-long ballet training could lead to better clinically relevant balancing abilities as well as vestibular-dependent spatial orientation capabilities; both of the benefits might be caused by positive influence of ballet training on the vestibular system function, and—possibly—its connectivity with temporal lobe regions responsible for vestibular-dependent orienting in space.
Exercising the Right Side of the Brain Might Help Protect against Alzheimer’s Disease

1 Institute of Neuroscience and School of Psychology, Trinity College Dublin, The University of Dublin, Dublin, Ireland 2 School of Psychological Sciences, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia 3 Global Brain Health Institute, Institute of Neuroscience, Trinity College, The University of Dublin, Dublin, Ireland 4 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany 5 Visual Attention Lab, Brigham & Women’s Hospital, Harvard University, Cambridge, MA, United States
Journal of Reproduction and Development

Soy-based formula contains high concentrations of the isoflavone genistein. Genistein possesses estrogenic and tyrosine kinase inhibitory activity and interferes with cellular proliferation and development. To date, the acute and chronic effects of genistein on ovarian and uterine development have not been fully elucidated. In this study, mice at postnatal day 1 were subcutaneously injected with 100 mg/kg genistein for 10 consecutive days, and then their ovaries and uteri were collected on days 10, 21, and 90. Histological evaluation was performed after hematoxylin and eosin staining. The proliferating activity was indicated by the proliferating indicator protein Ki67. Results showed that the subcutaneous injection of genistein to neonatal mice induced the formation of multi-oocyte follicles and delayed the primordial follicle assembly in the ovaries. Genistein significantly enlarged the cross-sectional area of the uterine cavity and wall and disrupted the regularity between the uterine stroma and myometrium. Genistein exposure inhibited proliferative activity because fewer Ki67-positive nuclei were detected in ovarian and uterine cell populations than in the control. Furthermore, most ovaries from adult mice given neonatal genistein were without corpora lutea, and there appeared to be cystic follicles and hypertrophy of the theca, and cortical and medullary layers. Considering the high concentration of isoflavone in soy-based infant formulas and livestock feed, we suggest that the use of isoflavone-rich diets in humans and livestock receive closer examination.
A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets

Sentiment analysis is an important technique to interpret user opinion on products from text, for example, as shared in social media. Recent approaches using deep learning can accurately extract overall sentiment from large datasets. However, extracting sentiment from specific aspects of a product with small training datasets remains a challenge. The automatic classification of sentiments at aspect-level can provide more detailed feedbacks about product and service opinions avoiding manual verification. In this work, we develop two deep learning approaches to classify sentiment at aspect-level using small datasets.
Linguistic operation time computation in parallel processor scheduling using the 2-tuple fuzzy representation model

Scheduling is the art of ordering a number of tasks with a fair allocation of resources with the intent of ensuring that these tasks are executed in minimal time. The processors, on which these tasks are executed, can be identical or nonidentical. They may have same or different execution capabilities. In real life situations, these tasks are in need of different types of resources such as network bandwidth, memory and processor speed. These resources are generally attached to the processors and the tasks are executed or assigned to the appropriate processor based on a scheduling algorithm. The scheduling algorithm determines the total execution time required to complete all the tasks all the processors and tries to minimize this total completion time. However, in real life, the values of these scheduling resources are uncertain and therefore they affect the total completion times to varying degrees. As human beings naturally understand and express themselves using words, therefore to incorporate human factors in these scheduling problems, the values of these criteria were specified linguistically in a recent work. The system was designed as a combination of if-then rules and Mamdani inference mechanism was used to determine the crisp value of completion time. Furthermore, the number of if-then rules required to design the system were very large. However, the solution to a problem formulated by the linguistic variables should be in linguistic form. Therefore, in the present work we propose the use of 2-tuple fuzzy linguistic approach to find the solution of parallel processor scheduling involving linguistic data values. The proposed approach is capable of giving linguistic solution using a small sized if-then rule base.
Contemporary Dental Ceramic Materials, A Review: Chemical Composition, Physical and Mechanical Properties, Indications for Use

BACKGROUND: The high esthetic expectations from the prosthodontic restorations have directed the qualitative development of the materials towards the all-ceramic materials that are capable of replacing porcelain-fused-to-metal systems. AIM: This article reviews the literature covering the contemporary all-ceramic materials and systems with a focus on the chemical composition and materials’ properties; also it provides clinical recommendations for their use. RESULTS: The glass-matrix ceramics and polycrystalline ceramics are presented, as well as recently introduced machinable materials, all-zirconia and resin-matrix ceramics. The specific properties of zirconia, such as transformation toughening, stabilisation of the crystallographic structure, low-temperature degradation and factors affecting the zirconia’s ageing, are emphasised. CONCLUSION: The favourable properties of the resin-matrix ceramics, such as modulus of elasticity similar to dentin, shock-absorbing characteristics and high resilience and fracture resistance, are also covered in this article.
18-month follow-up of randomized controlled trial of parent and child enhancement program

ABSTRACT Objective: This study aimed to evaluate the 18-month effectiveness of the Parent and Child Enhancement (PACE) program through an 18-month follow-up of the participants. Methods: Participants included parents and children who participated in the PACE program in 2013–2014, where they were randomized into intervention (n = 76) and control (n = 73) groups. At the 18-month follow-up, participating parents completed questionnaires on their children’s learning and behavior, and their own parenting. Children were individually assessed on their preschool concepts. There were 44 intervention group and 11 control group participants with complete data. Results: The results suggested that the intervention group participants were able to maintain their program gains at 18-month follow-up in children’s learning, behavior and parents’ own parenting, while there were no significant differences across the three time points for the control group participants. Conclusion: The results provided some initial evidence on the sustainability of the PACE program.
A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder

Deep neural networks have been successfully applied to many data mining problems in recent works. The training of deep neural networks relies heavily upon gradient descent methods, however, which may lead to the failure of training due to the vanishing gradient (or exploding gradient) and local optima problems. In this paper, we present SEvoAE method based on using Evolutionary Multiobjective optimization (EMO) algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way. SEvoAE is able to achieve accurate feature representation with good sparseness by globally simultaneously optimizing two conflicting objective functions and allows users to flexibly design objective functions and evolutionary optimizers. We compare results of the proposed method with existing architectures for seven classification prob- lems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.
Cancer-related distress in unselected women with newly diagnosed breast or ovarian cancer undergoing BRCA1/2 testing without pretest genetic counseling

Abstract Background: Genetic testing is increasing in patients newly diagnosed with cancer. This study investigated the levels, course and predictors of cancer-related distress, defined as intrusion and avoidance, in women undergoing BRCA1/2 testing without pretest genetic counseling shortly after a diagnosis of breast or ovarian cancer. Material and methods: Unselected for family history or age, 259 women with breast cancer and 50 women with ovarian cancer, underwent BRCA1/2 testing shortly after diagnosis. Cancer-related distress was measured with the Impact of Event Scale before and after genetic testing. In order to identify predictors of distress, the subscale scores were regressed on baseline predictor variables including sociodemographic and medical variables, perceived social support, and decisional conflict regarding genetic testing. Results: The mean levels of intrusion and avoidance were in the moderate range both before and after genetic testing with a statistically significant decline during follow-up. Younger age, shorter time since diagnosis, lower levels of social support, and a diagnosis of ovarian cancer predicted higher levels of both intrusion and avoidance. In addition, higher levels of decisional conflict and living with a partner predicted higher levels of intrusion. Conclusions: Women having genetic testing shortly after a diagnosis of breast or ovarian cancer had a moderate mean level of cancer-related distress, which decreased with time. Health personnel offering genetic testing to newly diagnosed women with breast or ovarian cancer should be aware of the potential predictors for increased cancer-related distress identified in this study: younger age, less perceived social support, higher levels of decisional conflict regarding genetic testing, and living with a partner.
Targeting inflammation in diabetic nephropathy: a tale of hope

ABSTRACT Introduction: Diabetic nephropathy (DN) is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Beyond the new anti-diabetic drugs that possess markedly cardiovascular and renal protective effects, no novel direct therapies for DN have become available on the market in the last twenty years. Recently well-designed clinical trials for the treatment of DN, with attractive pathogenetic rationale, e.g. bardoxolone and atrasentan, were canceled or stopped because of safety concerns or lack of reaching the end points, respectively. Areas covered: In this review, we focus on the involvement of inflammation in the pathogenesis of DN. We update information from recent experimental and clinical studies that reported beneficial effects of several agents targeting chemokines, cytokines, transcription factors and kinases as well as several compounds with anti-inflammatory properties on DN. Expert opinion: Inflammation plays a key role in the DN progression. Preclinical studies have identified several anti-inflammatory molecules that effective decrease albuminuria and/or proteinuria. However, limited clinical trials in humans have been performed to confirm these results. Inhibitors of CCL2/CCR2, IL-1β and JAK/STAT pathways, and Nrf2 inducers are promising therapeutic options to improve the renal outcome of patients with DN, but appropriate clinical trials are necessary.