Miracle-dz/UniFAR-FTUs huggingface dataset:
Finger ECG based Two-phase Authentication Using 1D Convolutional Neural Networks

This paper presents a study using 1D convolutional neural networks (CNNs) for ECG-based authentication. A simple CNN structure is used to both learn the features and do the classification automatically. Two types of CNNs are used in classification as a two-phase process. The “general” CNN is constructed based on global data and used as the preliminary screening, while “person-specific” CNN is constructed using single individual's data and applied as the fine-grained identification. The two-phase identification enables efficient recognition while guarantees a high specificity. Finger ECG signals are collected in different sessions using a mobile device. The proposed algorithm is tested on both within and across session data sets, and on different sample sizes. Results show that the proposed method achieves promising performance in authentication, with a 2.0% EER over 12000 beats. Due to its simple nature, the proposed system is highly applicable for practical application.
Multi-View Gait Recognition Based on a Spatial-Temporal Deep Neural Network

This paper proposes a novel spatial–temporal deep neural network (STDNN) that is applied to multi-view gait recognition. The STDNN comprises a temporal feature network (TFN) and a spatial feature network (SFN). In TFN, a feature sub-network is adopted to extract the low-level edge features of gait silhouettes. These features are input to the spatial–temporal gradient (STG) network that adopts a STG unit and a long short-term memory unit to extract the STG features. In SFN, the spatial features of gait sequences are extracted by multilayer convolutional neural networks from a gait energy image. The SFN is optimized by classification loss and verification loss jointly, which makes inter-class variations larger than intra-class variations. After training, the TFN and the SFN are employed to extract temporal and spatial features, respectively, which are applied to multi-view gait recognition. Finally, the combined predicted probability is adopted to identify individuals by the differences in their gaits. To evaluate the performance of the STDNN, extensive evaluations are carried out based on the CASIA-B, OU-ISIR, and CMU MoBo data sets. The best recognition scores achieved by STDNN are 95.67% under an identical view, 93.64% under a cross-view, and 92.54% under a multi-view. State-of-the-art approaches are compared with the STDNN in various situations. The results show that the STDNN outperforms the other methods and demonstrates the great potential of the STDNN for practical applications in the future.
Localisation of Colorectal Polyps by Convolutional Neural Network Features Learnt from White Light and Narrow Band Endoscopic Images of Multiple Databases

Algorithms for localising colorectal polyps have been studied extensively; however, they were often trained and tested using the same database. In this study, we present a new application of a unified and real-time object detector based on You-Only-Look-Once (YOLO) convolutional neural network (CNN) for localizing polyps with bounding boxes in endoscopic images. The model was first pre-trained with non-medical images and then fine-tuned with colonoscopic images from three different databases, including an image set we collected from 106 patients using narrow-band (NB) imaging endoscopy. YOLO was tested on 196 white light (WL) images of an independent public database. YOLO achieved a precision of 79.3% and sensitivity of 68.3% with time efficiency of 0.06 sec/frame in the localization task when trained by augmented images from multiple WL databases. In conclusion, YOLO has great potential to be used to assist endoscopists in localising colorectal polyps during endoscopy. CNN features of WL and NB endoscopic images are different and should be considered separately. A large-scale database that covers different scenarios, imaging modalities and scales is lacking but crucial in order to bring this research into reality.
Incremental reconstruction of water-tight surface from unorganized points

Most surface reconstruction algorithms require to input all sample points during a single process to construct a surface, but this requirement limits their applications in an incremental surface reconstruction under a consecutive point acquisition process. In this paper, we propose an incremental water-tight surface reconstruction algorithm, which allows the surface to be updated along with the incremental construction of the Delaunay triangulation and guarantee to output a water-tight surface. The core functioning structures, called regular umbrella-covered graph (RUCG) and constrained umbrella (CU ), are defined to represent the dynamic reconstruction process. Experiments are carried out to demonstrate the robustness of the proposed algorithm to reconstruct surface from sparse, dense, little noisy point clouds and point clouds of discrete objects. Comparisons with other Delaunay-Voronoi based algorithms are presented to support the effectiveness and superiority of the proposed algorithm. The proposed algorithm is quite simple, fast and non-parametric, and its complexity is only related to the 3D Delaunay triangulation of the points.
Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task

We present the contribution of the ONLP lab at the Open University of Israel to the UD shared task on multilingual parsing from raw text to Universal Dependencies. Our contribution is based on a transition-based parser called ‘yap – yet another parser’, which includes a standalone morphological model, a standalone dependency model, and a joint morphosyntactic model. In the task we used yap‘s standalone dependency parser to parse input morphologically disambiguated by UDPipe, and obtained the official score of 58.35 LAS. In our follow up investigation we use yap to show how the incorporation of morphological and lexical resources may improve the performance of end-to-end raw-to-dependencies parsing in the case of a morphologically-rich and low-resource language, Modern Hebrew. Our results on Hebrew underscore the importance of CoNLL-UL, a UD-compatible standard for accessing external lexical resources, for enhancing end-to-end UD parsing, in particular for morphologically rich and low-resource languages. We thus encourage the community to create, convert, or make available more such lexica in future tasks.
A Dynamic App Anti-Debugging Approach on Android ART Runtime

App anti-debugging techniques can protect app behavior from being leaked to attackers, thus reducing the possibilities of being compromised. Apps are vulnerable to being debugged in an insecure Android environment such as an emulator or a device with a rooted Android ROM. Researchers have proposed various Android Runtime (ART) instrumentation toolkits to trace app behavior. However, little research has been done to protect apps from being traced by those toolkits. In this paper, we introduce an approach to help apps combat being debugged dynamically. We collect potential check points and verify their integrity to protect them from being tampered with. Weimplement the approach and present the evaluation results on Android 8. The results show that the anti-debugging approach can defeat the tracking of app behavior effectively and efficiently.
Smartphone Based Human Breath Analysis from Respiratory Sounds

Human breath analysis plays important role for diagnosis and management of pulmonary diseases to guarantee normal health. The critical task is to distinguish normal and abnormal lung sounds. This research work presents a scheme for breath analysis used to detect irregular patterns occurred in respiratory cycles due to respiratory diseases. After de-noising breath segments using wavelet de-noising method, intrinsic mode functions are extracted with complete ensemble empirical mode decomposition (CEEMD). Instantaneous frequency (IF) and instantaneous envelope are extracted to get robust features for classification. The study contains breath samples captured using smartphone under natural setting. The data set contains 255 breath cycles. For cycle classification, Bag-of-word was applied to group segments based features. The support vector machine (SVM) was applied on randomly partitioned data samples. Experiments resulted with performance accuracy of (75.21%±2) for asthmatic inspiratory cycles and (75.5%±3%) for complete Respiratory Sounds (RS) cycle with diagnostic odds ratio (DOR) of 20.61% and 13.S7% respectively.
Identification of Optimum Panel of Blood-based Biomarkers for Alzheimer’s Disease Diagnosis Using Machine Learning

With the increasing number of people living with Alzheimer’s disease (AD), there is a need for low-cost and easy to use methods to detect AD early to facilitate access to appropriate care pathways. Neuroimaging biomarkers (such as those based on PET and MRI) and biochemical biomarkers (such as those based on CSF) are recommended by international guidelines to facilitate diagnosis. However, neuroimaging is expensive and may not be widely available and CSF testing is invasive. Bloodbased biomarkers offer the potential for the development of a low-cost and more time efficient tool to detect AD to complement CSF and neuroimaging as blood is much easier to obtain. Although no single blood biomarker is yet able to detect AD, combinations of biomarkers (also called panels) have shown good results. However, a large number of biomarkers are often needed to achieve a satisfactory detection performance. In addition, it is difficult to reproduce reported results within and across different study cohorts because of data overfitting and lack of access to the datasets used in the studies. In this study, our focus is to identify an optimum panel (in terms of the least number of blood biomarkers to meet the specified diagnostic performance of 80% sensitivity and specificity) based on a widely accessible data set, and to demonstrate a testing methodology that reinforces reproducibility of results. Realizing a panel with reduced number of markers will have significant impact on the complexity and cost of diagnosis and potential development of cost-effective point of care devices.
Terahertz Spectroscopic Identification of Ligusticum chuanxiong Hort and Ligusticum chuanxiong Hort. cv. Fuxiong

In this work, 240 herbal medicine samples of Ligusticum chuanxiong Hort and Ligusticum chuanxiong Hort. cv. Fuxiong were measured by terahertz time-domain spectroscopy and analyzed by the sparse autoencoder and the LargeVis algorithm. The results show that the advanced algorithm used in this paper can get a classification accuracy rate of 100% with good visualization. This technology can be used to qualitatively identify similar herbal medicines.
Modeling Perceptual Aliasing in SLAM via Discrete–Continuous Graphical Models

Perceptual aliasing is one of the main causes of the failure for simultaneous localization and mapping (SLAM) systems operating in the wild. Perceptual aliasing is a phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious measurements to be fed to the SLAM estimator, which typically results in incorrect localization and mapping results. This problem is exacerbated by the fact that those outliers are highly correlated, in the sense that perceptual aliasing creates a large number of mutually consistent outliers. Another issue stems from the fact that most state-of-the-art techniques rely on a given trajectory guess (e.g., from odometry) to discern between inliers and outliers, and this makes the resulting pipeline brittle, since the accumulation of error may result in incorrect choices and recovery from failures is far from trivial. This paper provides a unified framework to model perceptual aliasing in SLAM and provides practical algorithms that can cope with outliers without relying on any initial guess. We present two main contributions. The first is a discrete–continuous graphical model (DC-GM) for SLAM: The continuous portion of the DC-GM captures the standard SLAM problem, while the discrete portion describes the selection of the outliers and models their correlation. The second contribution is a semidefinite relaxation to perform inference in the DC-GM that returns estimates with provable sub-optimality guarantees. Experimental results on standard benchmarking datasets show that the proposed technique compares favorably with state-of-the-art methods while not relying on an initial guess for optimization.
Stress Resilience Measurement With Heart-Rate Variability During Mental And Physical Stress

Stress management is particularly important for healthcare of modern people. In stress research, heart-rate variability (HRV), indicating the change of time intervals in successive heart beats, significantly contributed due to its close relationship with autonomic nervous system. However, the adaptive response to stress, also known as stress resilience, has not been studied much yet. We collected electrocardiogram during mental and physical stress, experimentally designed by mental arithmetic tasks and physical activities for 14 healthy subjects. As a result, we found that resting HRV parameters, particularly associated with the parasympathetic activity, had significant positive correlations with reactivity and recovery from mental and physical stress. These HRV parameters can be used as a measure of stress resilience quantitatively. Our findings suggest that these parameters can help one’s stress management by enabling to predict the adaptive response to upcoming stressful events.
Observability Degree-Based AUV Single Beacon Navigation Trajectory Optimization with Range-Only Measurements

Aiming at the problem of autonomous underwater vehicle navigation trajectory optimization using single beacon location under direct route condition, a nonlinear system model for AUV single beacon navigation is established, and the linearized system model with error states is constructed by polar coordinate transformation and simplification. Then, current disturbance is considered. To find out the optimum path to utilize range-only measurements, a novel observability degree-based analysis method is proposed, which gives a quantitative insight into convergence characteristics of the error states by using the eigenvalues of the normalized error covariance matrix. Simulation experiments are done to test convergence characteristics of AUV integrated navigation error states with single beacon range-only measurements under direct route control conditions. The experimental results show that the proposed control method is effective, and it has an important engineering application value and provides us with an optimized path.
Investigation on the Angle and Spectral Dependence of the Internal and the External Quantum Efficiency of Crystalline Silicon Solar Cells and Modules

To improve energy yield predictions and to fully understand the physical behavior of solar cells, it is necessary to investigate the effects caused by a change in the angle of incidence. All relevant angle-dependent effects are presented in this paper and angular correction factors are introduced. A new analytical model is presented, which includes internal reflection, free carrier absorption, and other parasitic absorption processes. The angular effects were exemplarily studied for the case of a very simple planar solar cell sample and a standard module sample. With an effective angle approach, the variation of the internal quantum efficiency in the standard solar module could be emulated and the results were verified with the help of a ray tracing tool developed for this purpose. The influence of the different angular factors on the current density of a standard module at an incidence angle of 70° is finally presented.
Module differences of glucose metabolic brain network among Alzheimer’s disease, Parkinson’s disease dementia, Lewy body dementia and health control*

Dementia has become a serious disease in elderly population. Graph theory based brain network analysis method is considered as a popular and universal technique in the field of the neurosciences. It can help neuroscientists to investigate the neuropathology of dementia, especially to understand dementia subtypes. However, brain network analysis for various dementia subtypes at the same time is still limited. The aim at this study is to investigate the similarities and differences in brain network modular parameters of three dementia subtypes: Alzheimer’s disease (AD), Parkinson’s disease dementia (PDD), Lewy body dementia (DLB). Health control has also been compared. All subjects of health control(HC), AD, PDD and DLB groups were from Huashan Hospital. Using topological analysis algorithms, we found that HC brain was divided into 4 modules; AD brain was divided into 4 modules; PDD brain was divided into 6 modules; and DLB brain was divided into 10 modules, which demonstrated differences in these dementia subtypes. We also found that right thalamus area was scattered in all dementia subtypes. After using the seed point correlation analysis, it can be seen that the connections between right thalamus and subcortical, frontal gyrus were enhanced and the connections with the occipital gyrus was attenuated in dementia subtypes. As a result, findings in this paper are expected to be useful for neuroscientists to further understand the pathology of AD, PDD and DLB.
The Correlation Analysis Between DTI Network Parameters and AVLT Scale Scores of Alzheimer’s Disease

Neuroimaging and neuropsychology are employed to investigate the pathological features and clinical characteristics of Alzheimer’s disease (AD) in order to find a method for the precise treatment. Diffusion tensor imaging (DTI) provides a non-intrusive examination of cranial nerve diseases which can help us observe the microstructure of neuron fibers. Building the brain network provides a chance to reveal the significance of specific brain region and the relevance among different regions. In this study, we propose a completely novel method to analyze AD. First whole brain network is built on the basis of a novel segmentation atlas, and global graph theoretical parameters are calculated to evaluate the characteristic of whole brain. Then graph theoretical parameters of specific brain regions are extracted based on whole brain network. Finally neuropsychology scale are employed and we analyze the correlation between graph theoretical parameters of specific regions and scale scores. Our results illustrate the connection between neuroimaging data and neuropsychological scores, and provide a reasonable explanation for the potential connection between clinical performance and physiological brain lesions of AD patients.
Fibroblast Growth Factor‐23 and Heart Failure With Reduced Versus Preserved Ejection Fraction: MESA

Background Higher fibroblast growth factor‐23 (FGF‐23) levels are associated with incident heart failure (HF) in MESA (the Multiethnic Study of Atherosclerosis). FGF‐23 is also associated with left ventricular hypertrophy. Whether the FGF‐23 association with HF is similar for heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF) is not well established. Methods and Results We studied 6542 participants (mean age 62±10 years, 53% women, mean estimated glomerular filtration rate of 81±18 mL/min per 73 m2) from MESA who were free of cardiovascular disease at baseline (2000–2002). HF events were ascertained by an adjudication committee for a median follow‐up of 12.1 years. We classified HF events as HFrEF (ejection fraction [EF] <50%) or HFpEF [EF] ≥50%) at the time of diagnosis. Cox proportional hazard regression was used to compute hazard ratios and 95% confidence intervals for the association between baseline serum FGF‐23 and incident HFrEF and HFpEF. A total of 134 events were classified as HFpEF, 151 HFrEF, and 49 unknown EF. Following imputation, 149 were classified as HFpEF, 176 HFrEF, and 291 participants had HF (34 participants had HFpEF then HFrEF). In the fully adjusted model, higher FGF‐23 levels were associated with incident HFpEF but not with HFrEF (hazard ratio 1.29, 95% confidence interval, 1.08–1.54) versus (hazard ratio 1.04, 95% confidence interval, 0.84–1.29) for each 20 pg/mL higher serum FGF‐23 concentration. Conclusions FGF‐23 association with HF is driven by the association with HFpEF but not with HFrEF in a population‐based cohort. Further studies are needed to determine the pathological mechanisms mediating this association.
Assessing the Performance of Model-Based Energy Saving Charging Strategies in Li-Ion Cells

Li-ion batteries are widely employed as sources of portable energy. Advanced Battery Management Systems (ABMSs) rely on models to optimize battery performance (e.g. time-charge, life-time). The aim of this work is to assess if an energy efficiency improvement with respect to standard charging protocols can be obtained from model-based optimization in the time or in the frequency domain. Time-domain optimization is first considered and it is shown that only negligible improvements can be obtained. Then, motivated by successful experiments (e.g. [1], [2]), frequency-domain optimization is also investigated. Within this context, we prove that linear models are inadequate for providing any improvement. Surprisingly, even the mostly used electrochemical model available in literature (P2D), fails to capture the energy dissipation reduction with sinusoidal input current. These results show that currently available models are not suitable for the design of model-based energy saving strategies.
A Distributed Optimization Strategy for Large Scale Oil and Gas Production Systems

In this paper, we consider the problem of real-time optimization of an oil and gas production network. The production network is often made of several wells from different reservoir sections producing to a common processing facility. In this work, we exploit this spatial structure of the system and cast the production optimization problem into a distributed optimization problem and solve it using Lagrangian decomposition. We extend previous work on decomposition for static real-time production optimization problem by considering a dynamic real-time optimization problem. We demonstrate the applicability of the proposed method using a gas-lifted well network with shared resources and common capacity constraints.
An Adaptive Markov Random Field for Structured Compressive Sensing

Exploiting intrinsic structures in sparse signals underpin the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (i.e., the ability to fit a wide range of signals with diverse structures) and adaptability (i.e., being adaptive to a specific signal). Most existing approaches, however, often only achieve one of these two properties. In this paper, we propose a novel adaptive Markov random field sparsity prior for CS, which not only is able to capture a broad range of sparsity structures, but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements. To maximize the adaptability, we also propose a new sparse signal estimation, where the sparse signals, support, noise, and signal parameter estimation are unified into a variational optimization problem, which can be effectively solved with an alternative minimization scheme. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method in recovery accuracy, noise tolerance, and runtime.