Miracle-dz/UniFAR-FTUs huggingface dataset:
Block Chain Based Internet of Medical Things for Uninterrupted, Ubiquitous, User-Friendly, Unflappable, Unblemished, Unlimited Health Care Services (BC IoMT U6 HCS)

The most burning topic of today, calls for a holistic solution that is reliable, secure, privacy preserved, cost effective Cloud storage that can tide over the turbulent conditions of the rapidly budding digital storage technologies. This send an outcry for a devoted solution, in the form of an individualized, patient-centric care - IoMT that augments precise disease identifications, decrease in errors, reduction in costs of care through the support of technology, allows patients to direct health information data to doctors,manage drugs, keep Personal Health Records, caters to remote medical supports Care, provides proactive approach to preserving Good Health, improves and Accelerates Clinician Workflows, empowers extreme connectivity due to better automation and perceptions in the DNA of IoMT functions. But IoMT adoption is like a rose with thorns like constraints of increased administrative costs, deficiency of universal data access, present-day electronic medical records. The BCT is used in the framework to overcome the security issues of IoMT through the use of latest encryptions. Furthermore, this framework harnesses the benefits of Block Chain like reduced cost, speed, automation, immutability, near-impossible loss of data, permanence, removal of intermediaries, decentralization of consensus, legitimate access to health data, data safekeeping, accrual-based imbursement mechanisms, and medical supply chain efficacy. The outcomes in this paper are (i)A systematic investigation of the current IoMT, Block Chain and Cloud Storage in Health Care;(ii) Explore the challenges and necessities for the confluence of Block Chain (BC), Internet of Medical Things (IoMT), Cloud Computing (CC);(iii)Formulate the requirements necessary for the real-time remote Health Care of one-to-one care structure, which, supports the vital functions that are critical to the Patient Centric Health Care;(iv) Design and develop a novel BC IoMT U6 HCS (Block Chain based Internet of Medical Things for Uninterrupted, Ubiquitous, User-friendly, Unflappable, Unblemished, Unlimited Health Care Services) Layered Architecture, to support the vital functions critical for Patient Centric Health Care and (v) Implement and test with the previous established and proven techniques. The integrity of the Layered Architecture is validated with the already existing ones in terms of audit performances. The results from the Layered Architecture are validated and are proven to be competent in achieving safe auditing and surpass the former ones. The technology is in the sprouting phases, it is perilous that affiliates of the Health Care community realize the rudimentary ideas behind Block Chain, and detect its feasible impact on the future of patient centric medical care. Finally, and most importantly, this paper also gives a panoramic view on the current research status, and imminent directions of Secure Internet of Medical Things Using Block Chain.
Adaptive Transform Learning and Joint Sparsity Based PLORAKS Parallel Magnetic Resonance Image Reconstruction

Parallel magnetic resonance (MR) imaging is an important acceleration technique based on the spatial sensitivities of array receivers. The recently proposed Parallel low-rank modeling of local k-space neighborhoods (PLORAKS) approach uses the low-rank matrix model based on local neighborhoods of undersampled multichannel k-space data for reconstruction purposes. The joint total variation (JTV) regularization term was then combined with the PLORAKS model to improve the quality of reconstructed images. To further improve the quality of parallel MR imaging, we propose combining adaptive transform learning and joint sparsity with the PLORAKS model to obtain two algorithms, and reconstruction problems are solved by using the alternating direction method of multipliers (ADMM) and conjugate gradient techniques. The experimental results show that the two proposed algorithms can achieve higher performance than the PLORAKS algorithm and the PLORAKS-JTV algorithm with the JTV regularization term in terms of the signal-to-noise ratio (SNR), normalized root mean square error (NRMSE), high-frequency error norm (HFEN), and structural similarity index measure (SSIM).
Extended target detection and localization in 802.11ad/y radars

In this work, we consider an opportunistic mmWave radar obtained by adding a radar receiver to a 802.11ad/y device and we study the problem of detecting and localizing multiple extended (along both the range and Doppler dimensions) targets back-scattering the signal emitted by the communication transmitter. We derive an iterative subspace-based detector/estimator which extracts the prospective targets one-by-one from the superimposed received echoes, after cleaning up the previously-detected signal components. A short-range low-mobility application is discussed to validate the proposed solution.
IoT-based Measurement for Smart Agriculture

Smart agriculture is increasingly seen as a solution to global sustainability problems such as global warming, waste of water resources, excessive use of pesticides, and low economic activity. The core of this technology is the acquisition of data from the soil, crop, and climate to act in the production. Several solutions exist, but many are proprietary, high cost, hard to install, maintain, and integrate with third-party solutions. This paper presents an IoT technology set applied to the acquisition of agricultural data using open source solutions such as FIWARE and LoRaWAN, which allow extensive customization and integration with advanced weather forecasting, Machine Learning, and real-time dashboard services. The results obtained by the combination of different tools and platforms in pilots located in Brazil and Europe reveal a high versatility of the IoT technology applied to smart agriculture.
Post COVID-19 Global Macrotrends in the pedagogical practice to achieve Student Outcomes-“ICACIT”

The suspension of the on-site educational process due to the temporary lockdown of Educational Institutions provoked scenarios with their due reactions and repercussions of emergency against the Global Crisis as a result of the COVID-19 virus spread. Since Perú is in the midst of this current crisis, the Education sector asked some questions based on ICACIT accreditation model – such as: Which global macrotrends can be applied in the context of a Post COVID-19 pedagogical practices, in order to achieve Student Outcomes (SOs) from ICACIT, the accreditation model? Three categories were identified based on the previous development: I) Global macrotrends related to the Education Sector; II) The Post COVID-19 pedagogical practice, as a role assumed by the professor involved in the different education spaces in accordance with the different didactive approaches (academicist, technological, cultural interpretative, socio-critical and socio-formative); and III) Student Outcomes (SOs) from ICACIT, the accreditation model. Global macrotrends were selected to be applied in The Post COVID-19 pedagogical practice that contribute in the accomplishment of 12 ICACIT SOs and were consequently classified into three areas: i) Crosscutting Macrotrend (public awareness), ii) the Macrotrend of Post COVID-19 pedagogical practices (Disrupting Education: the assessment of progress, harnessing innovation, multiple senses, co-creation, instant entrepreneurship, the User Experience business-focused model approach for education, and gamification), and iii) the Macrotrend of Support (Networking & Technology).
Twitter Bot Detection with Reduced Feature Set

Online social networks provide a novel channel to allow interaction between human beings. Its success has attracted interest in attacking and exploiting them through a wide range of unethical activities, such as malicious actions to manipulate users. One of the methods to carry out these abuses is the use of bots on Twitter. Recent examples of bots influencing public opinion in the election process demonstrate their potential harm to the democratic world. Such malicious behavior needs to be checked and its effects should be diminished. Recently, machine learning (ML) classifiers to distinguish between real and bot accounts have proven advances. Thus, in this work four ML algorithms were tested using a public dataset and a few expressive features based on simple user profile counters for the classification of bots on Twitter. We measured their performance compared to one state–of–the–art bot detection work. The classifier accuracy was considered homogeneous with a mean of 0.8549 and 0.1889 of standard deviation. Besides, all multiclass classifiers obtained AUCs greater than 0.9 indicating a practical benefit for bot detection on Twitter.
Simulation of a Hydraulic Pressure Regulator Used in Automated Casting and Crystallization under Pressure Process

The controlled object was a hydraulic press used for casting with crystallization under pressure. Liquid and crystallizing metal was pressurized as specified by a control program. The pressure ranged from 0 to 400 MPa for 4…7 sec. The study presented optimal hydraulic press PID control settings. A flow chart of the automated pressure application to crystallizing metal was proposed. Advanced instruments controlled and monitored the process; they scanned the process variables in the specified range and with the required sampling rate and visualized them as curves. The automation devices efficiently controlled process equipment, compared the acquired and target values and made adjustments as required. The hydraulic drive block diagram was developed to create a correct transfer function of the closed-loop system. MatLab was used for the hydraulic drive simulations. The process was optimized and the adjustment factors were identified with Simulink. The study results apply to the design of computerized control systems for casting with crystallization under pressure processes.
Correction Algorithm for False Depth Values in Generating Point Cloud

Various multimedia systems have adopted the techniques related to point cloud in order to provide the immersive sense to users. In those systems, the depth values are very crucial because those dominantly affect the sense of reality. To increase the quality of point cloud by enhancing the depth images, various algorithms have been proposed. Whereas conventional methods remove the invalid depth information in construction of point cloud, we propose an algorithm to correct the invalid depth value. Simulation results show that the proposed algorithm outperforms other conventional techniques objectively and subjectively.
Automatic calcaneus fracture identification and segmentation using a multi-task U-Net

Calcaneus is the bone in the foot that bears most of the body weight and calcaneus fracture is the most common type of tarsal bone fractures. Plain radiograph examination is usually the first step of calcaneus fracture diagnosis because of its convenience and low cost. A multi-task U-Net is proposed in this paper to develop a computer aided calcaneus fracture diagnosis system. Our approach is an end-to-end CNN for identification and segmentation of calcaneus fracture, which uses regularization of the two tasks for mutual performance enhancement. First, a novel radiograph normalization method to obtain scale rotation invariance under different monochrome type is employed. Second, a classification header with feature from decoder and encoder is added to U-Net for multitask. Finally, a conditional dice-loss which can promote model performance under rough-ground-truth supervision is adopted in training. Experiments show that the network predicts fracture regions more precise than the rough ground-truth and identifies fracture with sensitivity of 99.53% and specificity of 98.59%.
Partially-Connected Hybrid Beamforming for Multi-User Massive MIMO Systems

Due to the high power consumption and hardware cost of radio frequency (RF) chains, the conventional fully-digital beamforming will be impractical for large-scale antenna systems (LSAS). To address this issue, hybrid beamforming has been proposed to reduce the number of RF chains. However, the fully-connected structure assumed in most hybrid beamforming schemes is still cost-intensive. Recently, the partially-connected structure employing notably fewer phase shifters has received considerable attention in both academia and industry. But the design of partially-connected hybrid beamforming has not been fully understood, especially in multi-user systems. In this article, we directly address the challenging non-convex non-smooth partially-connected hybrid beamforming design problem with individual signal-to-interference-plus-noise ratio (SINR) constraints and unit-modulus constraints in a multi-user massive multiple-input multiple-output (MIMO) system. An iterative alternating algorithm based on a penalty method is proposed to obtain a stationary point, which inevitably has relatively high computational complexity. Thus, two low-complexity algorithms are then proposed by utilizing matrix approximation. Numerical results demonstrate significant performance gains of the proposed algorithms over existing hybrid beamforming algorithms. Moreover, the proposed low-complexity algorithms can achieve near-optimal performance with dramatically reduced computational complexity.
A data-driven methodology to assess the accumulation risk in agricultural insurance contracts

This paper presents a data-driven approach allowing the insurance companies operating in the agricultural sector to assess the accumulation risk related to harmful meteorological events. The approach is based on the idea that the crucial piece of information necessary to consistently evaluate the accumulation risk is given by the knowledge of the characteristic spatial extension of the potentially harmful events. A procedure for estimating this quantity using historical weather time series is proposed.
Spatial-Temporal Topic Model for Cold-Start Event Recommendation

Event recommendation has attracted an increasing attention with the popularity of event-based social networks (EBSNs). The previous studies mainly focus on exploiting various contextual information to alleviate the cold-start problem in event recommendation. However, the interactions between different contextual factors, such as time, location and content, have not been modeled jointly. In this paper, we investigate the relationships among time, location and organizer in EBSNs, and propose a Spatial-Temporal Topic Model (STTM) for cold-start event recommendation. STTM can capture user’s interests on content and geographical space changing over time, and users have different event and venue topic distributions at different times in STTM. We perform an experimental evaluation on the real-world EBSNs dataset. The experimental results show the significant improvements of our method over other comparison methods, especially when dataset is more sparse.
Time Characteristics of Initial Stages for Aerosols Diffusion in Channels of Ventilation Systems

The processes of aerosols diffusion for turbulent flows in channels of industrial ventilation systems are considered. To describe the motion of discrete spherical particle in homogeneous turbulent flow, we apply the Basset equation. To analyze the characteristics of the turbulent flow, the time Lagrange correlation coefficients and the Lagrange functions of the energy spectrum are used. The time correlation coefficient of the main flow is approximated by the exponential dependence. The process of aerosols diffusion is conditionally divided into three stages: induction, transition and basic. In the induction period, the particles are accelerated by the Stokes forces from zero velocity to the main flow velocity, while the diffusion process is practically absent. In the transition period, the diffusion process occurs at variable diffusion coefficient. After the initial stages, the basic diffusion process of aerosol particles begins at constant diffusion coefficient, which can be investigated by known methods. The time characteristics of the initial periods in aerosols diffusion were estimated. The approximate expression was obtained for the diffusion coefficient of aerosol particles in the transition period. It is recommended when placing the concentration control means both the length of the concentration equalization path and the distance traveled by the particles during the induction and transient periods should be considered. When the length of the ventilation channel is relatively small, or the concentration control means are installed at sufficiently small distance from the source of emissions, the results of the control should be corrected.
Sensor-fusion and deep neural networks for autonomous UAV navigation within orchards

With the increase of population in the world, the demand for quality food is increasing too. In recent years, increasing demand and environmental factors have heavily influenced the agricultural production. Automation and robotics for fruit and vegetable production/monitoring have become the new standard. This paper discusses an autonomous Unmanned Aerial Vehicle (UAV) able to navigate through rows orchard rows. The UAV is comprised of a flight controller (AP stack), a microcontroller for analog reading of different sensors, and an On-Board Computer (OBC). Pictures are taken through a camera and streamed through WiFi to a Ground Control Computer (GCC) running a convolution neural network model. Based on prior training, the model outputs three directions: RIGHT, LEFT and STRAIGHT. A moving average of multiple frames per second is extracted and sent to a build-in Proportional-IntegralDerivative (PID) controller on the UAV. After error correction from this feedback, controller sends the direction to the flight controller using MAVLink protocol’s radio channel overrides, thus performing autonomous navigation.
THE HEAT CAPACITIES FROM ANALYTICAL DISTRIBUTION FUNCTIONS

The heat capacities and Joule–Thomson coefficients have been evaluated using three known analytical expressions for radial distribution functions (RDFs). Using such approach not only tests the power of distribution functions theory in predicting the heat capacities and Joule–Thomson coefficients, but also specifies better expressions in determining these properties. To calculate these quantities, the variation of RDF with density and temperature is required. Therefore, we should have analytical expressions which explicitly present RDF as a function of temperature, density, and interparticle distance. It is shown that if an expression is used which properly presents RDFs as a function of interparticle distance, density, and temperature, it is possible to calculate heat capacities and Joule–Thomson coefficients from distribution function theory.
Design and Implementation RF Amplifier with Advanced Capabilities

The article considers the design process, as well as implementation of microwave amplifier for particular problem set. The design of the low-noise amplifiers started from S-parameters and basic parameters such us amplifier stability, gain, optimum noise is considered. Two-port network is applied in order to find expressions for the power gains and input and output impedances in terms of S-parameters. Microwave amplifiers are used for high power amplification at high microwave frequencies, they can amplify across broad spectrums of frequencies and suitable for use in broadband communication, instrumentation and electronic applications. Based on S parameters of the transistor and certain requirements for performance, the article gives a set of procedure for the design of a microwave transistor amplifier. The most important design considerations of microwave transistor amplifier are power gain, bandwidth, noise, stability requirements. The article includes two parts - the theory based on which all design steps are conducted, and the practical part including authentic results after implementation are compared with theoretical calculations.
Network Measurements for Web Tracking Analysis and Detection: A Tutorial

Digital society has developed to a point where it is nearly impossible for a user to know what it is happening in the background when using the Internet. To understand it, it is necessary to perform network measurements not only at the network layer (e.g., IP, ICMP), but also at the application layer (e.g., HTTP). For example, opening a single website can trigger a cascade of requests to different servers and services to obtain the resources embedded inside it. This process is becoming so complex that, to explore only one website, the number of communications can explode easily from tens to hundreds depending on the website. Inside those communications, there is an ever-increasing portion dedicated to web tracking, a controversial practice from the security and privacy perspective [1].
Board-level Intelligent Built-in Test Design of Digital Input Module by Improved BP Neural Network

The built-in test (BIT) technology plays a major role in heavy-duty gas turbine control system. The accuracy of BIT is a guarantee for the normal operation of the equipment. In the actual production process, conventional BIT has a relatively high false alarm rate. In this paper, an intelligent built-in test (BIT) design method was presented for digital input (DI) module using a back propagation (BP), which was optimized by improved adaptive gravitational search algorithm (IAGSA) for quantitative analysis. In order to improve defect of the basic gravitational search algorithm (GSA), a tent map for population chaos initialization was proposed. Firstly, the status information that can reflect the failure of the DI module was chosen as experimental data. Then, these data were handled by normalization. After that, multiple IAGSA-BP models were established with status information about DI module. The output of these models can fully represent the fault status of the DI module. Finally, the experimental results are shown to validate the effectiveness of the proposed approaches.
TSN enabled 5G Non Public Network for Smart Systems

Ultra-Reliable Low Latency Communication(URLLC) is the current requirement of today’s vertical industry as per 3GPP 5G standard such as smart or automotive industry, smart vehicle, smart home, remote surgery for eHealth and cloud robotics for Industry 4.0. These systems are based upon network infrastructure which will handle time critical data traffic. In Release 16, 3GPP has made an integration of the 5G system with IEEE 802.1 working group specifications covering Time Sensitive Networking (TSN). Organizations are presently utilizing this standard in their applications over different wired and wireless networks like ethernet, fiber and 5G networks. In this paper the authors have presented their successful utilization of TSN in their 5G 28 GHz radio link and tested the radio link with different use cases. They are also exploring the different systems available with them starting with PC processor and bluestacks / chromecast software to launch their applications over 5G link. Further, they are exploring the use of multiprocessor based equipments for the upgradation of their system. The inclusion of NPN(Non Public Network) in Release 16 encourages the developers/users to implement their smart 5G network without taking any resources from MNOs (Mobile Network Operator). The authors are emulating NPN and TSN assisted 5G catering for smart system like smart home and vehicle.