Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination of Sensory, Chromatographic, and Chemometrics Analysis of Volatile Organic Compounds for the Discrimination of Authentic and Unauthentic Harumanis Mangoes

This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.
Virtual Reality Sickness Predictor: Analysis of visual-vestibular conflict and VR contents

Predicting the degree of sickness is an imperative goal to guarantee viewing safety when watching virtual reality (VR) contents. Ideally, such predictive models should be explained in terms of the human visual system (HVS). When viewing VR contents using a head mounted display (HMD), there is a conflict between user's actual motion and visually perceived motion. This results in an unnatural visual-vestibular sensory mismatch that causes side effects such as onset of nausea, oculomotor, disorientation, asthenopia (eyestrain). In this paper, we propose a framework called VR sickness predictor (VRSP) using the interaction model between user's motion and the vestibular system. VRSP extracts two types of features: a) perceptual motion feature through a visual-vestibular interaction model, and b) statistical content feature that affects user motion perception. Furthermore, we build a VR sickness database including 36 virtual scenes to evaluate the performance of VRSP. Through rigorous experiments, we demonstrate that the correlation between the proposed model and the subjective sickness score yields ~72 %.
An Attenuation Adapted Pulse Compression Technique to Enhance the Bandwidth and the Resolution Using Ultrafast Ultrasound Imaging

Recent studies suggest that Resolution Enhancement Compression (REC) can provide significant improvements in terms of imaging quality over Classical Pulsed (CP) ultrasonic imaging techniques, by employing frequency and amplitude modulated transmitted signals. However the performance of coded excitations methods degrades drastically deeper into the tissue where the attenuation effects become more significant. In this work, a technique that allows overcoming the effects of attenuation on REC imaging is proposed (REC-Opt). It consists in compensating the attenuation effects at each depth in reception. Combined with coherent plane wave compounding (CPWC), REC-Opt was compared to the performance of conventional REC (without attenuation compensation) and CP. With experimental data at 3.25 cm depth in a phantom with an attenuation coefficient slope of 0.5 dB/MHz/cm and using an 8.5 MHz probe, REC-Opt enhanced the bandwidth by 40.6% compared to CP, against an enhancement of only 6% between REC and CP using the same excitation signal designed to provide a 42% increase in bandwidth. The bandwidth enhancements translated into axial resolution improvements of 30% and 3% for REC-Opt vs. CP and REC vs. CP, respectively. This study suggests that REC-Opt is an efficient method to overcome attenuation effects in soft tissues, knowing their attenuation coefficient.
Automatic refinement of large-scale cross-domain knowledge graphs

Knowledge graphs are a way to represent complex structured and unstructured information integrated into an ontology, with which one can reason about the existing information to deduce new information or highlight inconsistencies. Knowledge graphs are divided into the terminology box (TBox), also known as ontology, and the assertions box (ABox). The former consists of a set of schema axioms defining classes and properties which describe the data domain. Whereas the ABox consists of a set of facts describing instances in terms of the TBox vocabulary. In the recent years, there have been several initiatives for creating large-scale cross-domain knowledge graphs, both free and commercial, with DBpedia, YAGO, and Wikidata being amongst the most successful free datasets. Those graphs are often constructed with the extraction of information from semi-structured knowledge, such as Wikipedia, or unstructured text from the web using NLP methods. It is unlikely, in particular when heuristic methods are applied and unreliable sources are used, that the knowledge graph is fully correct or complete. There is a tradeoff between completeness and correctness, which is addressed differently in each knowledge graph’s construction approach. There is a wide variety of applications for knowledge graphs, e.g. semantic search and discovery, question answering, recommender systems, expert systems and personal assistants. The quality of a knowledge graph is crucial for its applications. In order to further increase the quality of such large-scale knowledge graphs, various automatic refinement methods have been proposed. Those methods try to infer and add missing knowledge to the graph, or detect erroneous pieces of information. In this thesis, we investigate the problem of automatic knowledge graph refinement and propose methods that address the problem from two directions, automatic refinement of the TBox and of the ABox. In Part I we address the ABox refinement problem. We propose a method for predicting missing type assertions using hierarchical multilabel classifiers and ingoing/ outgoing links as features. We also present an approach to detection of relation assertion errors which exploits type and path patterns in the graph. Moreover, we propose an approach to correction of relation errors originating from confusions between entities. Also in the ABox refinement direction, we propose a knowledge graph model and process for synthesizing knowledge graphs for benchmarking ABox completion methods. In Part II we address the TBox refinement problem. We propose methods for inducing flexible relation constraints from the ABox, which are expressed using SHACL.We introduce an ILP refinement step which exploits correlations between numerical attributes and relations in order to the efficiently learn Horn rules with numerical attributes. Finally, we investigate the introduction of lexical information from textual corpora into the ILP algorithm in order to improve quality of induced class expressions.
Compressor map regression modelling based on partial least squares

In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and an extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely the look-up table and artificial neural network (ANN). PLSO and PLSN are also compared with each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the ANN, especially for the extrapolated prediction. The computational time is also decreased sharply. Compared with PLSO, PLSN is characterized by a higher prediction accuracy and shorter computational time than PLSO. It is expected that PLSN could save computational time and also improve the accuracy of a thermodynamic model of a diesel engine.
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation

In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.
A Multi-model Fusion Framework based on Deep Learning for Sentiment Classification

With the development of the Internet, more and more data can be found on texts information. People produce texts information via writing blogs, product reviews, microblogs, film reviews and so on, which contain sentiments or opinions of the writer. User comments usually can reflect their intuitive feelings for a product. We can dig out relatively large value through sentiment analysis for these comments. Sentiment classification, as one of the most important tasks in sentiment analysis for many real world applications, is our main focus in this article. To improve the accuracy of sentiment classification, we propose a deep neural network fusion framework, which is composed of a multi-window CNN-LSTM model and a multi-window CNN-CNN model with the fusion of the probability of two models to generate final output. Experimental results instruct that our framework is quite feasible.
RSA Key Generation from Cancelable Fingerprint Biometrics

The current cryptographic algorithms are suffering from the drawback of maintaining the secrecy of private keys. These private keys must be taken care of so that the cost of privacy can be avoided. Stored private keys are generally secured by the user tailored passwords, which can be hacked by brute-force attacks. Also, it is quite a horrid issue for the user to play with those large keys. So, the major issue is to protect and manage such a long private key. The generation of cryptographic key from individual user's biometric feature is a viable option. In this way it is too difficult for the attacker to guess the cryptographic key without having access of the user's biometrics. But the problem is that once it gets compromised it goes to waste. To solve the aforesaid issues cancelable biometrics approach has been proposed which is a wonderful solution for cancelling and reissuing the biometrics template. In this paper, a way to generate cryptographic key from the cancelable fingerprint template of the user has been suggested. Shuffling based transformation method is applied on the minutia points extracted from the user's fingerprint to generate cancelable template. Then a pair of private and public key is generated for RSA cryptosystems from cancelable template. This cancelable template maintains the secrecy of fingerprint template and produce revocable key for asymmetric cryptography.
Investigation on the AC/DC Interactions in Voltage-Source Rectifiers and Current-Source Rectifiers

This paper investigates the interactions between the ac side and the dc-link in voltage-source rectifiers (VSRs) and current-source rectifiers (CSRs). The impedance in the ac side and dc-link is proved to be the critical role in the ac/dc interactions. It is found that the ac/dc interactions affect the low frequency range dynamics in VSRs while high frequency range in CSRs. Furthermore, together with the time delay effect, stability analysis of direct dc-link control is presented, which shows the impact on tuning controllers caused by the ac/dc interactions varies from VSRs to CSRs. Simulation and experimental results are given to validate the theoretical analysis.
Nurses and health-promoting self-care: Do we practice what we preach?

AIMS To examine the health-promoting behaviours performed by registered nurses (RNs), as well as workplace factors that influence participation in those behaviours. BACKGROUND Nurses have high levels of overweight/obesity and may not be engaging in health-promoting self-care. METHODS A cross-sectional Web-based survey collected information from 335 RNs regarding their physical activity, sedentariness and fruit/vegetable consumption. RESULTS More than half were overweight (34.1%) or obese (23.4%), and 80.1% were "sedentary" (≥3 hr sitting/day), particularly those working outside of direct patient care in management, research and education. Only 47.2% consumed 5+ servings of fruits/vegetables daily. Nurses who enjoyed their jobs (higher levels of compassion satisfaction) reported higher levels of physical activity (p = 0.03) and fruit/vegetable consumption (p = 0.02). CONCLUSION RNs who work outside of direct patient care might be at increased risk for sedentariness and obesity. RNs who enjoy their jobs may experience less stress and have more energy to exercise and to prepare/consume healthy meals. IMPLICATIONS FOR NURSING MANAGEMENT Nurse managers should practice self-care by engaging in exercise, proper nutrition and demonstrating work-life balance, both to protect their own health and to serve as role models for RNs in direct patient care.
Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks

We present a new method to generate fake data in unknown classes in generative adversarial networks (GANs) framework. The generator in GANs is trained to generate somewhat similar to data in known classes but the different one by modelling noisy distribution on feature space of a classifier using proposed marginal denoising autoencoder. The generated data are treated as fake instances in unknown classes and given to the classifier to make it be robust to the real unknown classes. Our results show that synthetic data can act as fake unknown classes and keep down the certainty of the classifier on real unknown classes meanwhile the classification capability of known classes is not degenerated, even improved.
Asymmetric Collaborative Bar Stabilization Tethered to Two Heterogeneous Aerial Vehicles

We consider a system composed of a bar tethered to two unmanned aerial vehicles (UAVs), where the cables behave as rigid links under tensile forces, and with the control objective of stabilizing the bar's pose around a desired pose. Each UAV is equipped with a PID control law, and we verify that the bar's motion is decomposable into three decoupled motions, namely a longitudinal, a lateral and a vertical. We then provide relations between the UAV s' gains, which, if satisfied, allows us to decompose each of those motions into two cascaded motions; the latter relations between the UAV s' gains are found so as to counteract the system asymmetries, such as the different cable lengths and the different UAV s' weights. Finally, we provide conditions, based on the system's physical parameters, that describe good and bad types of asymmetries. We present experiments that demonstrate the stabilization of the bar's pose.
Children With Cerebral Palsy Have Greater Stride-to-Stride Variability of Muscle Synergies During Gait Than Typically Developing Children: Implications for Motor Control Complexity

Background. There is mounting evidence that the central nervous system utilizes a modular approach for neuromuscular control of walking by activating groups of muscles in units termed muscle synergies. Examination of muscle synergies in clinical populations may provide insights into alteration of neuromuscular control underlying pathological gait patterns. Previous studies utilizing synergy analysis have reported reduced motor control complexity during walking in those with neurological deficits, revealing the potential clinical utility of this approach. Methods. We extracted muscle synergies on a stride-to-stride basis from 20 children with cerebral palsy (CP; Gross Motor Function Classification System levels I-II) and 8 children without CP, allowing the number of synergies to vary for each stride. Similar muscle synergies across all participants and strides were grouped using a k-means clustering and discriminant analysis. Results. In total, 10 clusters representing 10 distinct synergies were found across the 28 individuals. Relative to their total number of synergies deployed during walking, synergies from children with CP were present in a higher number of clusters than in children with typical development (TD), indicating significantly greater stride-to-stride variability. This increased variability was present despite reduced complexity, as measured by the mean number of synergies in each stride. Whereas children with CP demonstrate some novel synergies, they also deploy some of the same muscle synergies as those with TD, although less frequently and with more variability. Conclusion. A stride-by-stride approach to muscle synergy analysis expands its clinical utility and may provide a method to tailor rehabilitation strategies by revealing inconsistent but functional synergies in each child with CP.
Extreme SAT-based Constraint solving with R-Solve

We present the architecture of R-Solve, a high-performance constraint solver that extends the gains made in SAT performance over the past fifteen years on static decision problems to problems that require on-the-fly adaptation, solution space exploration and optimization. R-Solve brings together a modern, open SAT solver, HPC ideas in ensemble and collaborative parallel solving and a novel set of extensions supporting, for the first time, an efficient system for unrestricted incremental solving. These extensions, which we call Smart Repair, enable R-Solve to address problems in dynamic planning and constrained optimization involving complex logical and arithmetic constraints. We provide insight into the rationale and challenges of extending an aggressively optimized competitive solver to support a new class of problem, provide a performance milestone illustrating the potential of R-Solve and Smart Repair and discuss new frontiers for further HPC research in SAT-based constraint solving and constrained optimization.
Quickest Change-Point Detection Over Multiple Data Streams via Sequential Observations

The problem of quickly detecting the occurrence of an unusual event that happens on one of multiple independent data streams is considered. In the considered problem, all data streams at the initial are under normal state and are generated by probability distribution $P_{0}$. At some unknown time, an unusual event happens and the distribution of one data stream is modified to $P_{1}$ while the distributions of the rest remain unchange. The observer can only observe one data stream at one time. With his sequential observations, the observer wants to design an online stopping rule and a data stream switching rule to minimize the detection delay, namely the time difference between the occurrence of the unusual event and the time of raising an alarm, while keeping the false alarm rate under control. We model the problem under non-Bayesian quickest detection framework, and propose a detection procedure based on the CUSUM statistic. We show that this proposed detection procedure is asymptotically optimal.
Modules of Correlated Genes in a Gene Expression Regulatory Network of CDDP-Resistant Cancer Cells

Chemotherapeutic drugs have been used as important strategies in cancer treatment. However, chemotherapy-resistant tumors arise especially in relapsing and progressive disease. Understanding of mechanisms underlaying Cisplatin-CDDP chemotherapy resistance may help find new therapeutic targets to revert this phenotype. The aim of this work, through an integrative Systems Biology approach, is to optimize an in silico model of TFs-miRNAs gene expression regulatory network of CDDP-chemoresistant cancer cell lines. By identifying modules of co-expressed genes in this regulatory network we expect further understanding of CDDP-chemoresistant phenotype. A set of deregulated genes was determined for two CDDP-chemoresistant cancer cell lines by considering gene copy number and transcriptomics. These genes were used as input targets for the construction and fitting of a large scale ordinary differential equations (ODE) model using our biocomputational platform previously reported. Model optimization was performed using COPASI and modules of correlated genes were determined using WGCNA. A model of 108 deregulated target genes, 44 transcription factors and 21 miRNAs was successfully constructed and optimized. Eleven modules of correlated genes were determined along with their gene product annotation. This report contributes to the understanding of the complex regulatory networks of CDDP-resistance and the future design of therapeutic strategies to overcome drug resistance.
Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.
Deep Geometric Matrix Completion: A New Way for Recommender Systems

In the last years, Graph Convolutional Neural Networks gained popularity in the Machine Learning community for their capability of extracting local compositional features on signals defined on non-Euclidean domains. Shape correspondence, document classification, molecular properties predictions are just few of the many different problems where these techniques have been successfully applied. In this paper we will present Deep Geometric Matrix Completion, a recent application of Graph Convolutional Neural Networks to the matrix completion problem. We will illustrate MGCNN (a multi-graph CNN able to deal with signals defined over multiple domains) and we will show how coupling such technique with a RNN, a learnable diffusion process can be realized for reconstructing the desired information. Extensive experimental evaluation shows how Geometric Deep Learning techniques allow to outperform previous state of the art solutions on the matrix completion problem.