Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Provisioning Availability of NFV in Data Center Networks

Network function virtualization is a promising technique to greatly improve the effectiveness and flexibility of network services through a process named service function chain (SFC) mapping, with which network functions are deployed over virtualized and shared platforms in data centers. However, failures are quite common in data centers. Therefore, a practical and yet theoretically challenging issue in SFC mapping in such an environment is to manage the availability of the requests. In this paper, we present a framework to provision availability of SFC requests in a data center with multiple layers of connected devices, and the devices follow heterogeneous failure processes with the objective of minimizing resource usage. To expedite the process, we further propose an optimization problem of request mapping and backup estimation and solve it efficiently with an approximation algorithm. With simulations, we demonstrate the effectiveness of our proposed framework.
An $l_{1}-l_{1}$ -Norm Minimization Solution Using ADMM with FISTA

This paper discusses compressed sensing which reconstructs original sparse signal from observed data. Our approach formulates the weighted sum of $l_{1}$ -norm error and $l_{1}$ -norm regularization terms, and applies Alternating Direction Method of Multipliers (ADMM) to solve it. Many works employ ADMM for the $l_{1}-l_{1}$ -norm minimization problems, where ADMM obtains solutions in an iterative fashion for the problems formed as an augmented Lagrangian. The ADMM process is divided into three steps: an error minimization, a coefficient-norm minimization, and a dual variable update of an augmented Lagrangian. However, the coefficient-minimization step is not clear and replaced with an approximation. Our contribution is to adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) for the minimization step and achieves faster implementation than a conventional method.
Experimental Investigation of Textile Planar Capacitive Sensors

Capacitive sensors are the subject of sustained interest to researchers and developers, especially in the field of medical systems for continuous monitoring of vital parameters. This is mostly due to their specific features: low cost, nonintrusive and non-invasive, fast response, flexibility and reliability. Considering the advantages of the new materials (conductive textile), this paper presents initial laboratory experiments related to the evaluation of the performance of planar textile capacitive sensors as well as for the estimation of their applicability for sleep disorders monitoring.
Analysis of Newly Developed Third Harmonic Injection Mothod with Ovpmw Method for a PV System Connected Through 3-Level Diode Clamped Inverter to an Induction Motor

This paper clarifies a relative investigation of tweak strategies for 3 level NPC Voltage Source Inverter for a heap associated Photo Voltaic (PV) framework. The examination is completed on two sorts of adjustment systems utilized as a part of three level MLI, viz. Recently grew third harmonic injection technique and ovpmw adjustment. The motivation behind the review is to dangerously break down and arrange the issues related with existing DCMLI utilized. It is watched that the three level Diode Clamped Multilevel Inverter (DCMLI) experiences limit beat width and does-interface capacitor voltage unbalancing, when associated with the most astounding appraised PV framework. Also, this issue results in wasteful execution of DCMLI, accordingly aggravating the power quality. The examination likewise accentuates the current balance procedures, albeit well known for inverters however neglecting to meet the present prerequisites when associated with MLI. The perception is checked by planning and actualizing the model in MATLAB/Simulink and appropriate outcomes are shown.
Prediction of Visitors using Machine Learning

With the advance of machine learning and deep learning, lots of applications have been implemented so far. Prediction is one of them, which has been drawing lots of interests from researchers. In this paper, we implemented the method to predict visitors in a certain tourism place using machine learning. From our experiments, we could get some positive results showing its applicability in a real environment.
Locally Recoverable codes with local error detection

A locally recoverable code is an error-correcting code such that any erasure in a coordinate of a codeword can be recovered from a set of other few coordinates. In this article we introduce a model of local recoverable codes that also includes local error detection. The cases of the Reed-Solomon and Locally Recoverable Reed-Solomon codes are treated in some detail.
Part-based Multi-stream Model for Vehicle Searching

Due to the enormous requirement in public security and intelligent transportation system, searching an identical vehicle has become more and more important. Current studies usually treat vehicle as an integral object and then train a distance metric to measure the similarity among vehicles. However, these raw images may be exactly similar to ones with different identification and include some pixels in background that may disturb the distance metric learning. In this paper, we propose a novel and useful method to segment an original vehicle image into several discriminative foreground parts, and these parts consist of some fine grained regions that are named discriminative patches. After that, these parts combined with the raw image are fed into the proposed deep learning network. We can easily measure the similarity of two vehicle images by computing the Euclidean distance of the features from FC layer. Two main contributions of this paper are as follows. Firstly, a method is proposed to estimate if a patch in a raw vehicle image is discriminative or not. Secondly, a new Part-based Multi-Stream Model (PMSM) is designed and optimized for vehicle retrieval and re-identification tasks. We evaluate the proposed method on the VehicleID dataset, and the experimental results show that our method can outperform the baseline.
Saliency guided fast interpolation for large displacement optical flow

The optical flow estimation is still an open question nowadays. One of the bottlenecks of it is the interpolation speed. In this paper, a saliency guide fast interpolation method is proposed which is more than about 2 times faster than the traditional one. The method runs on CPU without any supervision or semantic segmentation information. To make it faster, a fast saliency detection method is introduced to separate the image into two parts. The non-saliency superpixels are interpolated faster with random search only. The salient superpixels are interpolated by propagation and random search. To keep it accurate, the relative initial movement is used to guide the search area when computing the affine model. A soft affine model evaluation is introduced to make the optical flow result more robust. Extensive experiments on challenging datasets MPI-Sintel and KITTI-15 show that our method is efficient and effective.
Hoard: A Distributed Data Caching System to Accelerate Deep Learning Training on the Cloud

Deep Learning system architects strive to design a balanced system where the computational accelerator -- FPGA, GPU, etc, is not starved for data. Feeding training data fast enough to effectively keep the accelerator utilization high is difficult when utilizing dedicated hardware like GPUs. As accelerators are getting faster, the storage media \& data buses feeding the data have not kept pace and the ever increasing size of training data further compounds the problem. We describe the design and implementation of a distributed caching system called Hoard that stripes the data across fast local disks of multiple GPU nodes using a distributed file system that efficiently feeds the data to ensure minimal degradation in GPU utilization due to I/O starvation. Hoard can cache the data from a central storage system before the start of the job or during the initial execution of the job and feeds the cached data for subsequent epochs of the same job and for different invocations of the jobs that share the same data requirements, e.g. hyper-parameter tuning. Hoard exposes a POSIX file system interface so the existing deep learning frameworks can take advantage of the cache without any modifications. We show that Hoard, using two NVMe disks per node and a distributed file system for caching, achieves a 2.1x speed-up over a 10Gb/s NFS central storage system on a 16 GPU (4 nodes, 4 GPUs per node) cluster for a challenging AlexNet ImageNet image classification benchmark with 150GB of input dataset. As a result of the caching, Hoard eliminates the I/O bottlenecks introduced by the shared storage and increases the utilization of the system by 2x compared to using the shared storage without the cache.
A Study on Applications for Scientific Experiments Using the VR Technology

From the late 1980s we have begun to hear the word "disinterest in science. " On the other hand, virtual reality (VR) is technology that makes artificial environment created by computer be perceived as reality. Modern VR involves three dimensions: presence / interaction / autonomy, and in modern VR we use various interfaces (multimodal interface) such as vision / hearing / taste / olfaction / vestibular sensation / somatosensory sensation. The VR is currently being utilized in various fields such as scientific visualization, medical care, education, training, tourism, and entertainment. In this study, we tried to make a prototype VR system that anyone can easily perform scientific experiments such as physics and chemistry with simple operation. Furthermore, considering the cost burden of the user, we suggested a new system on a platform which can be easily prepared by using a smartphone.
Device Simulation of Negative-Capacitance Field-Effect Transistors With a Ferroelectric Gate Insulator

We consider the method to simulate negative-capacitance field-effect transistors having a ferroelectric film as a gate insulator in the framework applicable to technology computer-aided design device simulators and propose a method with complete applicability. In the method, the behavior of the polarization in ferroelectrics is described by the Landau-Khalatnikov equation and it is solved simultaneously with the Poisson equation to obtain the distribution of the polarization and electrostatic potential. Also, the proposed method enables the device simulators to take into account a factor related to forming domain structures of the polarization.
Information as a Life Vest: Understanding the Role of Social Networking Sites for the Social Inclusion of Syrian Refugees

Upon arrival, refugees are in desperate need of information concerning the asylum process and governmental requirements. An increasing number of websites and mobile applications have been specifically developed to socially include refugees by informing them about their host country. However, many refugees also use social networking sites to search for integration-related information, as cultural adaptation of governmental websites is almost non-existent. The growing quantity and poor quality of information on social networking sites may interfere with the information needs of refugees, resulting in dissatisfaction and misinformation. In the long run, such developments threaten to hinder social inclusion processes. Our study draws on Grounded Theory to explore the information practices of Syrian refugees on Facebook. We identify the different trust-inducing conditions and strategies refugees adopt to verify information, as well as possible consequences and context factors. Such insights are important for designing information platforms that facilitate social inclusion.
In vitro and in vivo quantification of chloroprocaine release from an implantable device in a piglet postoperative pain model

Background The pharmacokinetic properties and clinical advantages of the local anesthetic chloroprocaine are well known. Here, we studied the pharmacokinetic profile of a new hydrogel device loaded with chloroprocaine to investigate the potential advantages of this new strategy for postoperative pain (POP) relief. Materials and methods We performed both in vitro and in vivo analyses by considering plasma samples of four piglets receiving slow-release chloroprocaine. To quantify chloroprocaine and its inactive metabolite 4-amino-2-chlorobenzoic acid (ACBA), a HPLC–tandem mass spectrometry (HPLC-MS/MS) analytical method was used. Serial blood samples were collected over 108 hours, according to the exposure time to the device. Results Chloroprocaine was consistently found to be below the lower limit of quantification, even though a well-defined peak was observed in every chromatogram at an unexpected retention time. Concerning ACBA, we found detectable plasma concentrations between T0 and T12h, with a maximum plasma concentration (Cmax) observed 3 hours after the device application. In the in vitro analyses, the nanogel remained in contact with plasma at 37°C for 90 minutes, 3 hours, 1 day, and 7 days. Chloroprocaine Cmax was identified 1 day following exposure and Cmin after 7 days, respectively. Additionally, ACBA reached the Cmax following 7 days of exposure. Conclusion A thorough review of the literature indicates that this is the first study analyzing both in vivo and in vitro pharmacokinetic profiles of a chloroprocaine hydrogel device and is considered as a pilot study on the feasibility of including this approach to the management of POP.
Design and Evaluation of a Course Recommender System Using Content-Based Approach

Finding a user relevant information among huge number of data that are available in web is a difficult process. Therefore, an information filtering technique is needed to help the users to find their desired contents. Recommender system is the most famous technique which is used nowadays in many websites to support the suggestions making process. This paper will explain how to design a course recommender system by using kNN and Naïve Bayes classification algorithms, and evaluate their performances. The proposed recommender system follows content-based approach, by building a user profile (model), based on his/her prior knowledge and actions like, enrolling and rating courses, and compare it with courses attributes to generate recommended courses.
Miniaturization of a Branch-Line Coupler Using Microstrip Cells

This article is devoted to miniaturization of the quadrature coupler device. A procedure for designing a miniature design based on replacing a section of a microstrip transmission line with a microstrip cell consisting of distributed capacitances and inductances having the same phase shift as the intercepted segment is described. Modeling of the proposed design and optimization of the parameters are carried out. The frequency characteristics illustrating the properties of the coupler are shown. A prototype with a center frequency of 1000 MHz was manufactured. The entire simulation process was performed using the AWR DE program. With the help of the vector network analyzer, the company R & S obtained experimental dependencies. The experimental results with a small error coincide with the results of the simulation. The area of the compact coupler turned out to be 74% less compared to the area of the device of traditional topology. It was noted that the reduction in the band of operating frequencies compared to the band of the traditional design decreased by only 3%.
Advanced co-sublimation hardware for deposition of graded ternary alloys in thin-film applications

CdTe photovoltaic devices with efficiency over 22% have been demonstrated. Sublimated CdTe photovoltaics with efficiency over 19% have been reported using graded alloying of Se in CdTe absorber films. Grading of alloy films has been identified as an important characteristic to achieve higher device performance using more complex device structures. An advanced co-sublimation source has been designed and developed to deposit highly controlled CdTe based ternary alloys. An advanced shutter mechanism enables changing the composition of the deposited films during sublimation. The hardware used for advanced co-sublimation and initial materials characterization is presented in this study.
Design and Analysis of a Two-Phase Permanent-Magnet-Assisted Switched Reluctance Motor

Due to some advantages such as low driving system cost compared with multiphase switched reluctance motor (SRM) and small torque ripple in comparison with single-phase SRM, two-phase SRM has been received more and more attention. However, it suffers from low torque and power density. In order to solve this problem, an improved two-phase permanent-magnet-assisted SRM is proposed based on E-core 8/10 structure. By means of permanent magnet located in stator yoke, output torque is obviously improved. In order to better investigate performance of proposed motor, basic structure and operating principle are firstly introduced. Then, the equivalent magnetic circuit is employed to build the mathematical models of torque and inductance. Furthermore, the rotor shape is optimized for self-starting ability. Finally, some comparisons are made based on finite element method (FEM) and experimental results.
A Compact Dual-band Antenna at Ka-band Frequencies for Next Generation Cellular Applications

This paper presents a new design of a compact-sized dual –band antenna operating at 28 GHz and 38 GHz for 5G cellular applications. Based on the slot-coupled patch configuration, the antenna exhibited a good impedance matching with reasonable gain of 5.5/6.5 dBi at 28/38 GHz within the band of interest. The measured total radiation efficiency of the antenna was 88.9%/83.7% at 28/38 GHz.
Finger-vein Sample Compression in Presence of Pre-Compressed Gallery Data

Compression settings for sample (probe) finger vein data in case of already pre-compressed gallery data are investigated. Inhomogeneous compression scenarios are assessed where probe data can be compressed with different compression technique and compression ratio compared to gallery data using 4 lossy compression schemes, 2 finger vein recognition schemes, and 2 data sets. Results obtained indicate that in case of JPEG2000 pre-compressed gallery, also sample images should be compressed in the same manner, while for JPEG pre-compressed gallery, the optimal sample compression setting depends on the dataset, on the target compression ratio, and on the recognition scheme employed.
Recovery of Linearly Mixed Sparse Sources from Multiple Measurement Vectors Using L1-Minimization

Multiple measurement vector (MMV) enables joint sparse recovery which can be applied in wide range of applications. Traditional MMV algorithms assume that the solution has independent columns or correlation among the columns. This assumption is not accurate for applications like signal estimation in photoplethysmography (PPG). In this paper, we consider a structure for the solution matrix decomposed into a sparse matrix with independent columns and a square mixing matrix. Based on this structure, we find the uniqueness condition for l1minimization. Moreover, an algorithm is proposed that provides a new cost function based on the new structure. It is shown that the new structure increases the recovery performance especially in low number of measurements.
Experimental Evaluation of A Novel Antenna Structure:Multi-Panel Massive MIMO

With evolution of wireless communication, the next generation (5G) cellular system emerged and aimed at providing three times higher spectrum efficiency compared with LTE-Advanced system for enhanced Mobile Broadband. Massive MIMO is considered as one of the most important technologies to achieve the purpose and its benefits have been proved by theoretical analysis and field trials. However, the antenna size and weight have become the bottlenecks on the deployment in the realistic network. To tackle this problem, a novel flexible antenna structure for massive MIMO, known as multi-panel massive MIMO, was proposed and attracted broad attention in 3GPP NR (New Radio). Currently the performance of the multi-panel massive MIMO is studied in literature by the numerical simulation, however the field measurements to characterize the actual performance of multi-panel massive MIMO have been scarce so far. In this article, we first present a detailed description of our developed multi-panel massive MIMO prototype and then provide our radiation pattern measurement results and field test results. These experimental measurement results not only justify the validity of the coverage beamforming pattern, but also demonstrate that the multi-panel massive MIMO can achieve significant throughout gain. Based on these measurement results, it can be seen that multi-panel massive MIMO will be a novel practical antenna array structure for massive MIMO flexible deployment in the next generation cellular systems.
Automated Generation of a Single Shot Detector C Library from High Level Deep Learning Frameworks

In designing accurate and high-performance artificial neural networks (ANN) topologies for embedded systems, several weeks of engineering work are required to properly condition the input data, implement architectures optimized both in term of memory and operations using any of the off-the-shelf deep learning frameworks and then test the models in proper-scale data driven experiments. Moreover, code implementing the layers needs to be mapped and validated on the target systems, requiring additional months of hard and hand-made engineering work. To shorten this inefficient and un-productive development procedure, an efficient and automated C library generation workflow has been created. This work presents the phases involved in the automated mapping of a Single Shot Object Detector (SSD) model on an embedded library, where low power consumption is the focus and memory usage needs to be properly handled and minimized when possible. The implementation aspects, dealing with the mapping of high-level functions and dynamic data structures into low-level logical equivalents in ANSI C are presented; in addition, a brief explanation of the validation process as well as a short summary on the link between memory usage and the implementations details of the detector are also provided.
Pattern-Shared Sparse Bayesian Learning for Channel Estimation in FDD Massive MIMO Systems

Channel state information (CSI) is indispensable to fully unleash the great potentials of massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a pattern-shared sparse Bayesian learning (PS-SBL) method for downlink CSI estimation in frequency division duplex (FDD) massive MIMO. To characterize the common sparsity shared by different antennas, we design a pattern-coupled Gaussian prior model. The coefficients in channel vector are divided into several groups of equal length. Each group is associated with one common hyperparameter such that the coefficients in the group bear the same sparsity. Furthermore, by exploiting the expectation maximization (EM) procedure, we develop an iterative algorithm for Bayesian inference, where we treat the channel coefficients as hidden variables and the hyperparameters as unknown parameters. As a result, the obtained posterior mean of channel vector is used as the channel estimate. Simulations show that the proposed PS-SBL method remarkably outperforms the counterparts, and can approach the performance bound enabled by the genie-aided least square (LS) method.