Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Three-Factor User Authentication and Key Agreement Scheme for Wireless Medical Sensor Networks

As wireless communication technology and semiconductor technology developed fast, the wireless medical sensor networks (WMSNs) have been applied to the modern health-care area at large. The physiological data can be obtained by medical sensor nodes deployed in the patient’s body and sent to the special devices of the health professionals through wireless communication. Thus, the status of a patient is monitored by the health professional in that way. However, there are still two important issues that how to guarantee secure communication and protect the privacy of the patient for the reason of the open feature of wireless communication. In this paper, initially, an improved three-factor user authentication scheme is proposed to overcome those flaws utilizing password, smart card, and biometric feature. Furthermore, formal security analysis shows that the proposed scheme defends against various security pitfalls. Finally, the comparison results with other surviving relevant schemes show that our scheme is more efficient in terms of computational cost, communication cost, and estimated time. Therefore, the proposed scheme is suitable for practical application in WMSN.
Single-trial ERP Feature Extraction and Classification for Visual Object Recognition Task

This paper describes a classification method of brain signals measured from a subject during visual stimuli presentation. The method is based on exploring the features of visual event related potentials (VERP) in both time and spatial domains. Independent component analysis (ICA) is used for extracting the basic phase-amplitude features. A method for artifact rejection is proposed for excluding the noisy trials. A category-level classifier based on a neural network is trained and its performance on a publicly available database is evaluated and analyzed.
Minimax differential dynamic programming: application to a biped walking robot

We have developed a robust control policy design and method for high-dimensional state spaces by using differential dynamic programming with a minimax criterion. As an example, we applied our method to a simulated five link biped robot. The results show lower joint torques using the optimal control policy compared to torques generated by a hand-tuned PD servo controller. Results also show that the simulated biped robot can successfully walk with unknown disturbances that cause controllers generated by standard differential dynamic programming and the hand-tuned PD servo to fail. Learning to compensate for modeling error and previously unknown disturbances in conjunction with robust control design is also demonstrated. We applied the proposed method to a real biped robot to optimize swing leg trajectories.
Speech enhancement based on the general transfer function GSC and postfiltering

In speech enhancement applications, microphone array postfiltering allows additional reduction of noise components at a beamformer output. Among microphone array structures, the recently proposed general transfer function generalized sidelobe canceller (TF-GSC) has shown impressive noise reduction abilities in a directional noise field, while still maintaining low speech distortion. However, in a diffused noise field, less significant noise reduction is obtainable. The performance is even further degraded when the noise is nonstationary. We present three postfiltering methods for improving the performance of microphone arrays. Two of them are based on single-channel speech enhancers and make use of recently proposed algorithms concatenated to the beamformer output. The third is a multichannel speech enhancer which exploits noise-only components constructed within the TF-GSC structure. An experimental study, which consists of both objective and subjective evaluation in various noise fields, demonstrates the advantage of the multi-channel postfiltering compared to single-channel techniques.
Noise improvement for different BP filter designs

In this paper an analysis of noise through four different approaches to filter design is performed. The voltage noise spectral density and root mean square (RMS) value of noise voltage for the simply designed (SD) active RC, noise optimized (NO) active RC, operational transconductance amplifiers (OTA-C) and switched capacitor (SC) filter design are calculated for fourth-order Chebyshev band-pass filter, with central frequency fc=4kHz, bandwidth 1kHz and pass band ripple $\alpha_{max}$=-0.1dB. For the achievement realization of all four designs a cascade of two second order sections are used. The analyses is performed using Matlab and SPICE (LTspice) programming tools.
Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks

The nasal cavity and paranasal sinuses present large interpa-tient variabilities. Additional circumstances like for example, concha bullosa or nasal septum deviations complicate their segmentation. As in other areas of the body a previous multi-structure detection could facilitate the segmentation task. In this paper an approach is proposed to individually detect all sinuses and the nasal cavity. For a better delimitation of their borders the use of an irregular polyhedron is proposed. For an accurate prediction the Darknet-19 deep neural network is used which combined with the You Only Look Once method has shown very promising results in other fields of computer vision. 57 CT scans were available of which 85% were used for training and the remaining 15% for validation.
Topology-Preserving Shape-Based Regression Of Retinal Layers In Oct Image Data Using Convolutional Neural Networks

Optical coherence tomography (OCT) is a non-invasive imaging modality that provides cross-sectional 3D images of biological tissue. Especially in ophthalmology OCT is used for the diagnosis of various eye diseases. Automatic retinal layer segmentation algorithms, which are increasingly based on deep learning techniques, can support diagnostics. However, topology properties, such as the order of retinal layers, are often not considered. In our work, we present an automatic segmentation approach based on shape regression using convolutional neural networks (CNNs). Here, shapes are represented by signed distance maps (SDMs) that assign the distance to the next object contour to each pixel. Thus, spatial regularization is introduced and plausible segmentations can be produced. Our method is evaluated on a public OCT dataset and is compared with two classification-based approaches. The results show that our method has fewer outliers with comparable segmentation performance. In addition, it has an improved topology preservation, which saves further post-processing.
Noninvasive ultrasonic transdermal insulin delivery in rabbits using the light weight cymbal array

The purpose of this study was to demonstrate US transdermal delivery of insulin in vivo using rabbits with a novel, low profile two-by-two US array based on the cymbal transducer. As a practical device, the cymbal array (f=20 kHz) was 37/spl times/37/spl times/7 mm/sup 3/ in size and weighed less than 22g. With a similar intensity (I/sub sptp/=100 mW/cm/sup 2/, 20% duty cycle), our goal was to determine if the same effect could be achieved with rabbits. Fourteen New Zealand White rabbit (2.7-3.4 kg) experiments were performed in three groups: two controls and one US with insulin exposure. The first group (control 1) had insulin placed in the reservoir with no US while the second group (control 2) had saline in the reservoir with US operating at I/sub sptp/=100 mW/cm/sup 2/ for 60 minutes. The third group (US-Insulin) was subjected to insulin with US exposure for 60 minutes (I/sub sptp/=100 mW/cm/sup 2/). At the commencement of the experiment, 0.3 mL of blood was collected from the ear vein to determine the blood glucose level (mg/dL) using a blood glucose monitoring system. The insulin reservoir was removed with the array after the US was turned off at 60 minutes exposure. For both control 1 and 2, the blood glucose level showed an increase at 15 and 30 minutes and remained at approximately +50 mg/dL. For the US-Insulin group, the glucose level was found to decrease -132.6/spl plusmn/35.7 mg/dL in 60 minutes. After the US and insulin were removed, the blood glucose level of US-Insulin group continued to decrease to -208.1/spl plusmn/29 mg/dL. These results indicate the feasibility of using a low cost, light weight Cymbal array for enhanced transdermal insulin delivery using US.
Hierarchical Meta-learning Models with Deep Neural Networks for Spectrum Assignment

In this paper we consider a data-driven approach and apply machine learning methods to facilitate frequency assignment. Specifically, a hierarchical meta-learning architecture that harnesses the predictive capability of both statistical and deep learning approaches is proposed to predict a diverse range of spectrum usage patterns. Using spectrum measurements, network simulations are conducted to evaluate the effectiveness of the proposed architecture. It is shown that the hierarchical meta- learning models with deep recurrent neural networks have great potential for predicting spectrum usage patterns to facilitate multi-tier spectrum assignments.
Automated decision-making with DMN: from decision trees to decision tables

Recent advances in artificial intelligence, especially the subfield of machine learning, is commonly cited as one of the driving forces for digital transformation and innovative business models. Ongoing research is focusing on embedding solutions based on machine learning into business processes which are commonly modelled using the BPMN standard. The Object Management Group has recently adopted the Decision Model and Notation standard. By using the Decision Model and Notation (DMN) it is possible to replace multiple decision points embedded in business processes. The purpose of this research is to provide a method to derive DMN decision tables from the corresponding machine learning model generated by the decision tree classifier. The development is conducted using the Python machine learning library scikit-learn and Camunda Modeler. This approach facilitates and automates the process of converting machine learning models into DMN tables.
Aperture coupled microstrip antenna with quasi-planner surface mounted horn

A new combination of aperture coupled microstrip antenna and a quasi-planner surface mounted short horn is demonstrated to increase the gain of patch antenna by 4.2 dB. The structure has 12.4% bandwidth. The presence of quasi-planner has improved the back-lobe level by 5.4 dB. The antenna is designed in the Ku-band.
Analysis of a Differential Noise Detection Filter in ECG Signals

Noise detection presents a big challenge in wearable heart monitor technology and beat detection. The distorted signal is hard to interpret and as a consequence, valuable information may be lost. In this paper, we present our research for developing a noise detection filter based on a differential filter. Our analysis offers in-depth evaluation of the optimal values of two critical parameters - window size and Signal-to-Noise threshold. Our final goal is to minimize errors in QRS detection and beat classification caused by noise-distorted data.
Multi-View Subspace Clustering With Block Diagonal Representation

Self-representation model has made good progress for a single view subspace clustering. This paper proposed the multi-view subspace clustering model based on self-representation. This model assumes that the samples from different classes are embedded in independent subspaces. Thus, the fused multi-view self-representation feature should be block diagonal, and a block diagonal regularizer with the complementarity of multi-view information is given. The model optimization algorithm by alternating minimization is proposed and its convergence without any additional assumption is proved. With the complementarity of multi-view information and the block diagonal property, our model will depict data more comprehensively than single view independently. The extensive experiments on public datasets demonstrate the effectiveness of our proposed model.
Analysis of Two‐Level Support Systems with Time‐Dependent Overflow—A Banking Application

In this paper, we analyze the performance of call centers of financial service providers with two levels of support and a time‐dependent overflow mechanism. Waiting calls from the front‐office queue flow over to the back office if a waiting‐time limit is reached and at least one back‐office agent is available. The analysis of such a system with time‐dependent overflow is reduced to the analysis of a continuous‐time Markov chain with state‐dependent overflow probabilities. To approximate the system with time‐dependent overflow, some waiting‐based performance measures are modified. Numerical results demonstrate the reliability of this Markovian performance approximation for different parameter settings. A sensitivity analysis shows the impact of the waiting‐time limit and the dependence of the performance measures on the arrival rate.
A Radiomics Approach to Analyze Cardiac Alterations in Hypertension

Hypertension is a medical condition that is well-established as a risk factor for many major diseases. For example, it can cause alterations in the cardiac structure and function over time that can lead to heart related morbidity and mortality. However, at the subclinical stage, these changes are subtle and cannot be easily captured using conventional cardiovascular indices calculated from clinical cardiac imaging. In this paper, we describe a radiomics approach for identifying intermediate imaging phenotypes associated with hypertension. The method combines feature selection and machine learning techniques to identify the most subtle as well as complex structural and tissue changes in hypertensive subgroups as compared to healthy individuals. Validation based on a sample of asymptomatic hearts that include both hypertensive and non-hypertensive cases demonstrate that the proposed radiomics model is capable of detecting intensity and textural changes well beyond the capabilities of conventional imaging phenotypes, indicating its potential for improved understanding of the longitudinal effects of hypertension on cardiovascular health and disease.
Semi-Supervised Learning With Structured Knowledge For Body Hair Detection In Photoacoustic Image

Photoacoustic (PA) imaging is a promising new imaging technology for non-invasively visualizing blood vessels inside biological tissues. In addition to blood vessels, body hairs are also visualized in PA imaging, and the body hair signals degrade the visibility of blood vessels. For learning a body hair classifier, the amount of real training and test data is limited, because PA imaging is a new modality. To address this problem, we propose a novel semi-supervised learning (SSL) method for extracting body hairs. The method effectively learns the discriminative model from small labeled training data and small unlabeled test data by introducing prior knowledge, of the orientation similarity among adjacent body hairs, into SSL. Experimental results using real PA data demonstrate that the proposed approach is effective for extracting body hairs as compared with several baseline methods.
Position-Based User-Centric Radio Resource Management in 5G UDN for Ultra-Reliable and Low-Latency Vehicular Communications

5G is expected to play the supporting role in the upcoming era of cooperative automated driving (CAD). Ultra-reliability and low latency are required in the uplink transmission of the periodic CAD-facilitating messages from automated vehicles (AVs) of high-density and high-mobility. This paper studies the feasibility of position-based radio resource management (RRM) in 5G ultra-dense networks (UDNs) with user-centric networking in addressing these challenges. Two main RRM tasks: virtual cell (VC) member association and time-frequency resource block (RB) allocation, are identified and the related performance metrics are defined. Targeting a typical freeway scenario in urban environment, a set of practical position-based strategies are proposed, and their performances are confirmed by simulation.
An Accurate Fast Fluid Dynamics Model for Data Center Applications

Traditional CFD is broadly useful in the design and operation of reliable and efficient data centers. It is nevertheless computationally expensive, particularly when employed for design optimization, which usually requires multiple simulations. To speed-up calculations while retaining essential airflow physics, several researchers have turned to an alternative CFD methodology, namely, fast fluid dynamics (FFD). FFD has been reported to be much faster than traditional CFD, but at an assumed (acceptable) trade-off of reduced accuracy. However, a recent comparison of FFD and traditional CFD for data center plenum applications produced nearly indistinguishable results and suggested that previously-reported FFD/traditional-CFD differences were due primarily to inconsistent: 1) advection schemes, 2) computational grids, and 3) turbulence models. The present paper extends this FFD/traditional-CFD comparison to the data center whitespace and confirms the finding that a true like-for-like comparison produces nearly identical predictions. Our FFD implementation utilizes a first-order upwind finite-volume scheme for advection like traditional CFD, so alternative advection schemes (e.g., semi-Lagrangian) are not considered further here. Likewise, the effect of grid choice on traditional-CFD predictions is well known. However, the effect of turbulence model for data center applications has not been reported extensively so we do consider this topic further here. We compare the standard $\mathrm{k}-\in$ and a simpler algebraic model to benchmark experimental data from a real data center. We find that the algebraic turbulence model predicts rack-inlet temperatures at a similar level-of-accuracy as the $\mathrm{k}-\in$ model for our fairly-simple-airflow reference data center.
Phonon Mean Free Path - Thermal Conductivity Relation in AlN

The challenging high temperature operation conditions for AlN based electronic devices such as UV LEDs and HEMTs may cause degradation and failure. In these applications, thermal conductivities of the material layers have a critical role in heat dissipation and device temperature reduction. Thermal conductivity and phonon mean free path calculations lead to better understanding of the thermal transport in such devices with small scales. This study aims to perform ab-initio calculations via VASP to obtain up to third-order force constants and feed them to Phono3py to calculate the phonons' contributions to the thermal conductivity of AlN. The results can be used to determine the onset of nanoscale thermal transfer in devices that consists of such materials and to determine a proper mean free path for nanoscale thermal simulations.
Optically synchronized oscillators for low phase noise microwave and RF frequency distribution

Various circuits dedicated to high spectral purity signal transmission using fiber optics are presented. Three application types are investigated: reference frequency distribution at 10 MHz, IF distribution at 800 MHz and microwave synthesized signals at 3.5 GHz. The reception circuit is an optically synchronized oscillator, which provides a good signal conditioning far from the carrier while maintaining the high input signal quality close to the carrier.
Extraction of Focused Topic and Sentiment of Financial Market by using Supervised Topic Model for Price Movement Prediction

For financial market participants, the current focused topic (Brexit, Federal Reserve Interest-Rate, U.S. and China trade war, etc.) and its sentiments (whether it is Risk-On or Risk-Off) is very important to decide investment strategies. In this study, we proposed extended topic model called supervised Joint Sentiment-Topic model (sJST) which using not only text data but also numeric data as a supervised signal to extract current focused topic and it's sentiment of market. By using the topic and sentiment weight of the market as a features, we apply several machine learning models to predict foreign exchange market price movement. Comparing the average accuracy over 32 currency pairs and prediction models, the result using sJST weight as features achieved 1.52% better performance than the results which use only historical prices as features. Furthermore, comparing the results limited to specific currency pairs which is difficult to predict when using only historical prices as features, the result using sJST weight as features achieved 3.64% better accuracy than the result which use only historical prices as features.
Augmenting Gabor-based Face Recognition with Global Soft Biometrics

Though face recognition using traditional (hard) biometrics has attracted massive research interest and received extensive studies, it still confronts degrading variability challenges and notably achieves lower performance compared with other biometric recognition forms like fingerprint and iris. Recently, a number of research studies have been interested in enhancing face recognition performance by all means of supplementary facial biometric traits or other biometric modalities. Thus, soft biometrics have been emerged as a new promising modality of biometrics and highlighted as likely viable and fusible traits for augmenting traditional/hard biometrics. This is due to the expected advantages of soft biometrics over the traditional biometric traits, such as the high collectability and invariance properties. Other than fusing different kinds of traditional traits to augment face recognition, adding soft biometrics to augment various traditional facial traits has yet gained little research attention. Hence, in this research, unlike the majority of existing work, we investigate the viability of global soft face biometrics in supplementing traditional (hard) biometrics and the efficacy of concurrently using absolute and relative descriptions as soft biometrics. We conduct a new soft biometric-based fusion scheme in feature-level for augmenting a traditional Gabor-based face identification/verification in different potential forensic scenarios, considering performance variability evaluation and comparison with the baseline performance of Gabor features in isolation.
Short-term Stock Price Prediction by Analysis of Order Pattern Images

Predicting the price movements of stocks based on deep learning and high-frequency data has been studied intensively in recent years. Especially, limit order book which describes the supply-demand balance of the market is used as features of a neural network. However, these methods do not utilize the properties of market orders. On the other hand, this study encodes information of time and prices of orders into images. This encoding method can take advantage of these properties. Then, we apply machine learning methods, convolutional neural network (CNN) and logistic regression (LR), to order-based features to predict the direction of short-term price movements. The results show that the execution has the highest prediction power than the order and cancellation information. Moreover, the difference between CNN and LR are small and depends on kinds of stocks.
Proceedings 2003 Joint Conference on Digital Libraries

The following topics are dealt with: music digital library; humanities digital library; scientific digital library; business digital library; multimedia collection; educational digital repository; medical digital library; cultural heritage collection; digital museum exhibition; digital resource management; document metadata creation; knowledge representation; data mining; information retrieval; open archives initiative; XML standards; knowledge portals; Internet; Web site; digital library protection.