Miracle-dz/UniFAR-FTUs huggingface dataset:
Uterine Catecholamines in Normal and Hypertensive Human Pregnancy

The uterine tissue content of the adrenergic transmitter norepinephrine decreases during normal gestation, as part of a functional uterine denervation. Uterine tissue catecholamines were measured in normal and hypertensive human pregnancy. Myometrial samples were obtained at hysterectomy in nonpregnant patients, or by wedge biopsy at the time of cesarean section, in normal and preeclamptic pregnant patients. Biopsies were taken of both placental and non-placental bed tissue in the pregnant patients. All samples were analyzed for catecholamines by a radioenzymatic assay. There was a significant decrease in uterine norepinephrine and dopamine, but no change in epinephrine in normal pregnant patients when compared with non-pregnant patients. The decrease in dopamine and norepinephrine was greater in the placental bed, suggesting a local augmentation of this process by the placenta. There was an increase in uterine dopamine and norepinephrine in patients with preeclampsia, but no change in epinephrine, when c.
Radio Resource Sharing and Edge Caching with Latency Constraint for Local 5G Operator: Geometric Programming Meets Stackelberg Game

The rapidly increasing demand in indoor small cell networks has given rise to the concept of local 5G operator (OP) for local service delivery. In this regard, we develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled small cell base stations (SBSs) with infrastructure sharing for local 5G OP networks. In such a network, the local 5G OP provides wireless network in indoor area and rent out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the cache intensity subject to latency constraint at each UE. Here, the cache intensity is defined as the product of the number of SBSs per unit area and the number of popular files stored in each SBS. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the subgame perfect equilibrium of Stackelberg game is obtained through the succesive geometric programming (SGP) method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. We show that the cost sharing problem can be mapped into a simplified “airport runway cost sharing problem”, in which the Shapley value can be computed efficiently. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks.
Automatic text summarisation of case law using gate with annie and summa plug-ins

Legal reasoning and judicial verdicts in many legal systems are highly dependent on case law. The ever increasing number of case law make the task of comprehending case law in a legal case cumbersome for legal practitioners; and this invariably stifles their efficiency. Legal reasoning and judicial verdicts will therefore be easier and faster, if case law were in abridged form that preserves their original meaning. This paper used the General Information Extraction System Architecture approach and integrated Natural Language Processing, Annotation, and Information Extraction tools to develop a software system that does automatic extractive text summarisation of Nigeria Supreme Court case law. The summarised case law which were about 20% of their original, were evaluated for semantic preservation and has shown to be 83% reliable. Keywords: Case law, text summarisation, text engineering, text annotation, text extraction
Text Extraction from Natural Scene Images using OpenCV and CNN

— The influence of exponentially increasing camera-embedded smartphones all around the world has magnified the importance of computer vision tasks, and gives rise to a vast number of opportunities in the field. One of the major research areas in this field is the extraction of text embedded in natural scene images. Natural scene images are the images taken from a camera, where the background is random, and the variety of colors used in the image may be diverse. When text is present in such type of images, it is usually difficult for a machine to detect and extract this text due to a number of parameters. This paper presents a technique that uses a combination of the Open Source Computer Vision Library (OpenCV) and the Convolutional Neural Networks (CNN), to extract English text from images efficiently. The CNN model is based on a two-stage pipeline that uses a single neural network to directly detect the characters in the scene images. It eliminates the unnecessary intermediate steps that are present in the previous approaches to this task making them slower and inaccurate, thereby improving the time complexity and the performance of the algorithm.
Cuidados de enfermería basados en evidencia y modelo de Betty Neuman, para controlar estresores del entorno que pueden ocasionar delirium en unidad de cuidados intensivos

Background: Delirium is a fluctuating disturbance in consciousness that is highly prevalent in the intensive care unit. The adult ICU of the Hospital Universitario de Neiva shows an incidence of 28% of delirium patients, this increases morbidity and mortality rates among critical ones; nonetheless, this can be prevented if intervention occurs. Objective: Determine the effectiveness of To determine the effectiveness of care applied, according to guide nursing care nursing care to prevent delirium in patients, who were hospitalized in the adult intensive care unit, the patients were exposed to environmental stressors. Methodology: A quantitative, prospective and pre-experimental study was carried out to 49 patients who received assessment with the help of a nursing care guide. Besides the guide, a pretest and a posttest were carried out to evaluate the patients’ perception on environmental stressors. Results: Three out of the 49 patients experienced delirium; this represents a cumulative incidence of 6.12 delirium cases x 100 patients (CI 95%: 1.67-16.1), evident with the Kaplan-Merier curve. Based on the proportion hypothesis testing (Z ₀.₀₅ = -1.65) and in contrast to previous results where the incidence of delirium was of 28%, the statistical evidence suffices to demonstrate that the presence of the syndrome is much less frequent in patients to whom the nursing care guide. Conclusions: The guide prevented the occurrence of delirium in 94% of the patients, regardless of their deliriogenic history, by controlling the continuous noise and artificial light. This is why, minimizing environmental stressors, which results in a more pleasant environment, is a fundamental part of nursing care for the prevention of such a syndrome.
REGIONAL-SCALE SEISMIC FRAGILITY ASSESSMENT BASED ON GAUSSIAN PROCESS REGRESSION

Seismic fragility assessment of building portfolios usually involves empirical approaches, or numerical, mechanics-based approaches applied to properly-sampled index buildings representative of defined structural classes. These approaches often neglect the effect of class variability on portfolio seismic risk estimates. Alternatively, metamodeling techniques can be adopted to surrogate complex mechanical analyses and to properly include class variability. However, commonly-used metamodels require the a priori definition of the functional form for the fitting and they quantify the uncertainty on the predictions of the output (e.g., fragility as a function of the geometry of a building) based on simplifying assumptions. In this study, Gaussian process regression is adopted to address these limitations. The proposed method is demonstrated for seismically-deficient RC school buildings with construction details typical of some developing countries (e.g., in Southeast Asia), for which real data is available. Gaussian processes estimating the fragility statistics of such schools are fitted based on thousands non-linear time-history analyses for over 100 building realisations within the structural class. To further increase the tractability of the methodology, alternative metamodels are defined based on numerical non-linear static (pushover) analyses or analytical “by hand pushover” through the Simple Lateral Mechanism Analysis (SLaMA) method. Four validation structures (outside the training set) are defined and analysed through the same approaches. Preliminary results from this study show predicted-to-“observed” errors below 10%, highlighting the accuracy of the fitted metamodels. Moreover, non-linear static approaches (SLaMA or numerical pushover), coupled with the capacity spectrum method, produce sound results, drastically reducing the computational burden in the model calibration.
Keynote: A Disquisition on Logic Locking

The fabless business model has given rise to many security threats, including piracy of intellectual property (IP), overproduction, counterfeiting, reverse engineering (RE), and hardware Trojans (HT). Such threats severely undermine the benefits of the fabless model. Among the countermeasures developed to thwart piracy and RE attacks, logic locking has emerged as a promising and versatile solution that is being adopted by both academia and industry. The idea behind logic locking is to lock the design using a “keying” mechanism; only the rightful owner has control over the locked design. Therefore, the design remains nonfunctional without the knowledge of the key. In this article, we survey the evolution of logic locking over the last decade. We introduce various “cat-and-mouse” games involved in logic locking along with its novel applications—including, processor pipelines, graphics processing units (GPUs), and analog circuits. We aim this article to be a primer for researchers interested in developing new logic-locking techniques and employing logic locking in different application domains.
Iot Based Bluetooth Smart Radar Door System Via Mobile Apps

In the last few decades, Internet of things (IOT) is one of the key elements in industrial revolution 4.0 that used mart phones as one of the best technological advances’ intelligent device. It allows us to have power over devices without people intervention, either remote or voice control. Therefore, the “Smart Radar Door “system uses a microcontroller and mobile Bluetooth module as an automation of smart door lock system. It is describing the improvement of a security system integrated with an Android mobile phone that uses Bluetooth as a wireless connection protocol and processing software as a tool in order to detect any object near to the door. The mob ile device is required a password as authentication method by using microcontroller to control lock and unlock door remotely. The Bluetooth protocol was chosen as a method of communication between microcontroller and mobile devices which integrated with many Android devices in secured protocol.
D2D-Enabled User Cooperation in Massive MIMO

In this article, we investigate device-to-device (D2D) communication-enabled user cooperation in massive multiple-input multiple-output (MIMO) systems. In frequency-division duplexing massive MIMO systems, cascaded precoding has been adopted to reduce the overhead of channel estimation and limited feedback. The inner precoder in cascaded precoding depends on the spatial covariance matrix of the massive MIMO channel and can be the same for the users that are close to each other. When the users are close to each other, high-quality D2D links can be expected among the users and the users can form a virtual MIMO receiver via D2D links. Since the users may require different services, the required transmission rates of those users can also be different. To improve the overall user experience, utility functions are adopted in this article and two D2D-enabled user cooperation schemes, including the data-symbol-information exchanging and receive-signal-information exchanging, are proposed, which can allocate the overall transmission rate among the users adaptively in terms of the required transmission rates of different users. In this way, the overall utility function can be maximized.
A Survey on Coping With Intentional Interference in Satellite Navigation for Manned and Unmanned Aircraft

Intentional interference in satellite navigation is becoming an increasing threat for modern systems relying on Global Navigation Satellite Systems (GNSS). In particular, critical applications such as aviation can be severely affected by un-detected and un-mitigated interference and therefore interference management solutions are crucial to be employed. Methods to cope with such intentional interference enclose interference detection, interference mitigation, interference classification, and interference localization. This paper offers a comprehensive survey of interference management methods developed in the last four decades by the research community. After reviewing the main concepts of GNSS-based navigation, the interference and interference management solutions are classified, with a particular focus on the two major threats in GNSS navigation, namely jamming and spoofing. Mathematical models, comparative tables for various interference management solutions, such as detection, localization, mitigation, and classification, as well as comparative numerical results based on several selected algorithms are also presented. We especially focus on algorithms relying on omni-directional antennas, which do not require additional specific antennas to be installed on the aircraft and thus reduce the costs of retrofit and installation.1
Source routing: Best rout using genetic algorithm

The success of distributed multimedia traffic presents many challenges to the network designer, certain bandwidth as well as bounds on delay, loss probability and jitter in order to maintain reception quality are issues of Constraint Base Routing (CBR). Flow routing requires deterministic path coordination in comparison with data routing. This paper presents a new technique for best CBR-rout path computation using genetic algorithm. Available bandwidth, delay jitter, and path availability are the fitness function, the genetic algorithm try to optimize. Source routed packets were established to hold the complete elected path in packet header. All studies made on NS2 simulator, where SARBE approach for rapid on-line estimation of available bandwidth shows performance optimization on OSPF algorithm for multimedia streaming applications.
Rancang Bangun Sistem Kendali Kompor Pintar Berbasis Aplikasi Google Assistant Menggunakan Metode Voice Recognition

ABSTRACT Gas stoves circulating in the community are the ones which are used manually. Users must be in front of the stove to control the gas stove. So that if the user is busy preparing the ingredients to be cooked, the user cannot control the gas stove along with the activity. In addition, the security level is still very poor, because often the user forget to turn off the stove or because of a gas leak. This makes circulating gas stoves less effective and efficient. This time, it is very necessary to use a practical gas stove, which is using voice commands. Therefore, it needs the creation of a smart stove operational control system using voice commands which combines technology and service and aims to improve the efficiency, comfort and safety of its users. This smart stove uses a PIR sensor to detect the presence of human movement so that it avoids negligence in use and has safety against gas leaks with gas sensors. In addition, the use of smart stoves is no longer done manually, such as on gas stoves in general. Smart stoves can be controlled remotely with voice recognition and assisted by using the Google Assistant found on a smartphone with the addition of applications such as Tasker, Auto Voice, Arduino Auto, Auto App, Join, and IFTTT. The test results show that the MQ-2 gas sensor is sensitive to LPG gas leakage, but the accuracy will be better when it detects LPG gas leak with a long leakage duration with an ADC value of around 800. Then testing the fire sensor shows that the fire sensor can detect all sizes of fire at an intermediate distance of 10-50 cm. Besides that, in testing PIR sensors that are used as human motion detectors, the detection distance reaches 5 m. In testing servo motors the formed angle has a result that is close to the actual angle. Then in testing the Bluetooth HC-05 module it shows that when conditions are unhindered, Bluetooth can be connected with a range of distances between 1-10 meters. Conversely, when there is a barrier, Bluetooth can be connected with a range of 1-8 meters. In addition, voice recognition with the Google Assistant on Android has a 100% success rate in receiving voice from a voice that has been sampled whether there is noise or no noise with the ideal distance and clear pronunciation of words. Therefore with accurate voice recognition, smart stoves can be controlled remotely by voice commands and assisted by using the Google Assistant on the smartphone with the addition of applications such as Tasker, Auto Voice, Arduino Auto, Auto App, Join, and IFTTT. Keyword : fire sensor, gas sensor, servo motor, PIR sensor, voice recognition
Generalized $M_{m,r}$ -Network: A Case for Fixed Message Dimensions

In this letter, we first present a class of networks named <italic>Generalized</italic> <inline-formula> <tex-math notation="LaTeX">${{M}}_{{ \textit {m, r}}}$ </tex-math></inline-formula><italic>-Network</italic> for every integer <inline-formula> <tex-math notation="LaTeX">${m} \geq 2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\forall {r} \in \{0, 1,\ldots, {m}-1\}$ </tex-math></inline-formula> and we show that every network of this class admits a vector linear solution if and only if the message dimension is an integer multiple of <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>. We show that the <italic>Generalized</italic> <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula><italic>-Network</italic> presented in the work of Das and Rai and the <italic>Dim-</italic><inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> <italic>Network</italic> introduced in the work of Connelly and Zeger which are generalizations to the <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula>-Network can be considered as special cases of Generalized <inline-formula> <tex-math notation="LaTeX">${M}_{\textit {m, r}}$ </tex-math></inline-formula>-Network for <inline-formula> <tex-math notation="LaTeX">${r}=1$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${r}={m}-1$ </tex-math></inline-formula> respectively. Then we focus on a problem induced by depending on integer multiples of <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> as message dimensions to achieve the linear coding capacity in the class of Generalized <inline-formula> <tex-math notation="LaTeX">${M}_{\textit {m, r}}$ </tex-math></inline-formula> (proven to be equal to 1). We note that for large values of <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>, packet sizes will grow beyond feasible thresholds in real-world networks. This motivates us to examine the capacity of the network in the case of fixed message dimensions. A study on the contrast among the impacts of fixed message dimensions in different networks of class <inline-formula> <tex-math notation="LaTeX">${M}_{\textit {m, r}}$ </tex-math></inline-formula>-Network highlights the importance of the examined problem. In addition to complete/partial solutions obtained for different networks of the class Generalized <inline-formula> <tex-math notation="LaTeX">${M}_{\textit {m, r}}$ </tex-math></inline-formula>-Network, our studies pose some open problems which make the Generalized <inline-formula> <tex-math notation="LaTeX">${M}_{\textit {m, r}}$ </tex-math></inline-formula>-Network an attractive topic for further research.
Hybrid attention convolution acoustic model based on variational autoencoder for speech recognition

In a communication system, smart home and other speech-based application, Automatic Speech Recognition (ASR) plays a crucial role, and its inputs usually are features extracted from a raw speech signal. A well-designed feature extractor could improve the accuracy and reduced the computation complexity. The feature extraction is a basic processing unit not only in ASR but also in Text to Speech (TTS), Speaker conversion and other conversion systems. Meanwhile, through the analysis of the feature extracted with a labeled phone in each frame, the result could be used for improving TTS as well. The goal of the project is to build a new acoustic model used in ASR system based on neural network. Moreover, in the case, compared with main streaming acoustics modules, the most significant difference is that autoencoder introduces which makes the training of acoustic module becoming semi-supervised learning from supervised learning. Feature extraction usually like Mel-frequency cpectrum coefficient (MFCC), Linear predictive coding (LPC), Warped Minimum Variance Distortionless response cepstral coefficients (WMVDRCC) are based on standard digital signal processing while neural network based feature extraction is uncommon. Moreover, Deep Neural Network (DNN) has been proved as an efficient ASR method used the DNN module as an acoustic module in the ASR system. Meanwhile, the acoustic module could be built with Convolution Neural Network (CNN) or Long short-term memory recurrent neural network (LSTM). In the master thesis, a hybrid attention mechanism is brought into acoustic. More specifically, time-frequency attention weighting in autoencoder, a front stage of the acoustic model. And channel attention weighting in a phone classifier, postage of acoustic model. For accelerating this semi-supervised later development in VGG-BLSTM structure, a regularization method is developed for dynamic loss combine weighting. In this method, a regularization item is added to loss, which not also dynamic control weighting between two loss, but also push the weight to a preset value for smooth and steady loss decreasing.
Merkle Search Trees: Efficient State-Based CRDTs in Open Networks

Most recent CRDT techniques rely on a causal broadcast primitive to provide guarantees on the delivery of operation deltas. Such a primitive is unfortunately hard to implement efficiently in large open networks, whose membership is often difficult to track. As an alternative, we argue in this paper that pure state-based CRDTs can be efficiently implemented by encoding states as specialized Merkle trees, and that this approach is well suited to open networks where many nodes may join and leave. At the core of our contribution lies a new kind of Merkle tree, called Merkle Search Tree (MST), that implements a balanced search tree while maintaining key ordering. This latter property makes it particularly efficient in the case of updates on sets of sequential keys, a common occurrence in many applications. We use this new data structure to implement a distributed event store, and show its efficiency in very large systems with low rates of updates. In particular, we show that in some scenarios our approach is able to achieve both a 66% reduction of bandwidth cost over a vector-clock approach, as well as a 34% improvement in consistency level. We finally suggest other uses of our construction for distributed databases in open networks.