Miracle-dz/UniFAR-FTUs huggingface dataset:
The magnitude and severity of abortion-related morbidity in settings with limited access to abortion services: a systematic review and meta-regression

Introduction Defining and accurately measuring abortion-related morbidity is important for understanding the spectrum of risk associated with unsafe abortion and for assessing the impact of changes in abortion-related policy and practices. This systematic review aims to estimate the magnitude and severity of complications associated with abortion in areas where access to abortion is limited, with a particular focus on potentially life-threatening complications. Methods A previous systematic review covering the literature up to 2010 was updated with studies identified through a systematic search of Medline, Embase, Popline and two WHO regional databases until July 2016. Studies from settings where access to abortion is limited were included if they quantified the percentage of abortion-related hospital admissions that had any of the following complications: mortality, a near-miss event, haemorrhage, sepsis, injury and anaemia. We calculated summary measures of the percentage of abortion-related hospital admissions with each complication by conducting meta-analysis and explored whether these have changed over time. Results Based on data collected between 1988 and 2014 from 70 studies from 28 countries, we estimate that at least 9% of abortion-related hospital admissions have a near-miss event and approximately 1.5% ends in a death. Haemorrhage was the most common complication reported; the pooled percentage of abortion-related hospital admissions with severe haemorrhage was 23%, with around 9% having near-miss haemorrhage reported. There was strong evidence for between-study heterogeneity across most outcomes. Conclusions In spite of the challenges on how near miss morbidity has been defined and measured in the included studies, our results suggest that a substantial percentage of abortion-related hospital admissions have potentially life-threatening complications. Estimates that are more reliable will only be obtained with increased use of standard definitions such as the WHO near-miss criteria and/or better reporting of clinical criteria applied in studies.
Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm

This paper aims to develop an accurate estimation technique for computing state of charge (SOC) of a lithium-ion battery using recurrent neural network algorithm. Nonlinear autoregressive with exogenous input (NARX) model is a well-known subclass of the recurrent neural network which has proven to be very effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARX neural network depends on the amount of input and output order as well as a number of neurons in a hidden layer. Therefore, this study presents an improved recurrent NARX neural network based SOC estimation with particle swarm optimization (PSO) algorithm for finding the best value of input delays, feedback delays and a number of neurons in a hidden layer. The proposed model uses three most significant factor such as current, voltage and temperature without considering battery model. The model robustness is checked at low temperature (0°C), medium temperature (25°C), and high temperature (45°C). The US06 drive cycle is selected for model training and testing. The effectiveness of the proposed approach is compared with the back-propagation neural network (BPNN) optimized by PSO based on the SOC error, root mean square error (RMSE) and mean absolute error (MAE) and average execution time (AET). The results prove that the proposed model has higher estimation speed and achieves higher accuracy in reducing RMSE and MAE by 53% and 50% than BPNN based PSO model at 25°C.
Consensus of third-order multi-agent systems with fixed topology and input delay

In this paper, the consensus problem is investigated for the third-order multi-agent system with input delay. Based on frequency domain analysis, the sufficient and necessary conditions for two kinds of third-order consensus protocols are obtained, respectively. Compared with the existing results, this paper focuses on finding the largest tolerable input delay which is showed by non-zero eigenvalues and scaling strengths. Simulation results illustrate the correctness of the results.
Analysis and Performance Optimization of LoRa Networks With Time and Antenna Diversity

Low power wide area network (LPWAN) technologies are increasingly catching the attention of the Internet-of-Things market and have brought the need for reliable knowledge about the performance of such networks. This paper is concerned with the performance and scalability of LoRa networks, a leading LPWAN technology. Several recently published articles have analyzed the ability of LoRa networks to scale, i.e., their ability to support increased traffic and number of nodes. This paper proposes to employ message replication and gateways with multiple receive antennas to achieve, respectively, time and spatial diversity. The paper presents the proposed schemes and evaluates them through theoretical analysis and computer simulations. Results show that LoRa networks are highly sensitive to the increase in user and traffic density, but both message replication and multiple antennas can enhance performance. Message replication has an optimum number of message copies for each network configuration, and its utilization is more beneficial in low-density networks, while the use of multiple receive antennas at the gateway is always beneficial.
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes

While designing the state space of an MDP, it is common to include states that are transient or not reachable by any policy (e.g., in mountain car, the product space of speed and position contains configurations that are not physically reachable). This results in weakly-communicating or multi-chain MDPs. In this paper, we introduce TUCRL, the first algorithm able to perform efficient exploration-exploitation in any finite Markov Decision Process (MDP) without requiring any form of prior knowledge. In particular, for any MDP with $S^c$ communicating states, $A$ actions and $\Gamma^c \leq S^c$ possible communicating next states, we derive a $O(D^c \sqrt{\Gamma^c S^c A T}) regret bound, where $D^c$ is the diameter (i.e., the length of the longest shortest path between any two states) of the communicating part of the MDP. This is in contrast with optimistic algorithms (e.g., UCRL, Optimistic PSRL) that suffer linear regret in weakly-communicating MDPs, as well as posterior sampling or regularised algorithms (e.g., REGAL), which require prior knowledge on the bias span of the optimal policy to bias the exploration to achieve sub-linear regret. We also prove that in weakly-communicating MDPs, no algorithm can ever achieve a logarithmic growth of the regret without first suffering a linear regret for a number of steps that is exponential in the parameters of the MDP. Finally, we report numerical simulations supporting our theoretical findings and showing how TUCRL overcomes the limitations of the state-of-the-art.
AKILLI TELEFON ALGILAYICILARI İLE AKTİVİTE TANIMA: ALGILAYICILARIN VE PENCERE BOYUNUN ETKİSİ HUMAN ACTIVITY RECOGNITION WITH MOBILE PHONE SENSORS: IMPACT OF SENSORS AND WINDOW SIZE

—With the help of the sensors available on smart phones and smart watches, inference on user context, particularly the activity, can be inferred. Raw data collected from the sensors enables the classification of human activities with machine learning algorithms. In studies focusing on mile activity recognition , usually the motion sensors, such as accelerometer and gyroscope are used. In this paper, our aim is to analyze the performance of actıvıty classification when these sensors are used separately or in combination. By using a dataset which is collected from fifteen participants including six different activities, we extract various features from raw data and afterwards supervised machine learning algorithms are used to train and validate the results. Five different classifiers and different validation methods are used for performance analysis.
Improving signal modulation recognition using principal component analysis and compressive sensing

In order to solve the problem of low recognition rate of communication signals under low signal to noise ratio(SNR), a new dimensionality reduction method is proposed, which is based on compressive sensing and principal component analysis. Compared with the traditional dimensionality reduction methods, such as principal component analysis and compressive sensing, the proposed method has greater noise reduction function. Using random forest as a classifier, the recognition rate of several communication signals reached 90% at SNR>-10dB.
Dimension of Riemann-Liouville fractional integral of Takagi function

In this paper, we mainly discuss the characteristics of a type of special function called Takagi function which was derived from Weierstrass function. We have proved this function is continuous but can not be differentiable on any subinterval. In other words, it has no bounded variation points even one. Then, we calculate its Hausdorff dimension and Box dimension is 1. Furthermore, its Riemann-Liouville fractional integral is also 1. Finally, some numerical and graphic results are provided to characterize Takagi function.
Data freshness model for P/S based sensor monitoring system

Publish-Subscribe(P/S) based sensor monitoring systems provide different quality-of-service according to clientserver policies. Using adaptive timing and data freshness models may increase the flexibility of services in these systems. In this paper, we propose two timing models for sensor network based emergency detection P/S Systems in which the timing constraints and data freshness can be dynamically adjusted for user needs. The performance of these models is tested in a real testbed setup. The experimental results reveal that the proposed models can meet the timing and data freshness demands of the clients for applications which acquire physical data from scalar sensors with 2–10s sampling rates.
Secure in-network big data provision with suspension chain model

Big data raises strong demands on a network infrastructure to support the efficient data provision and retrieval. Information-Centric Networking (ICN) approach is an emerging approach to satisfy this demand, where big data is ubiquitously cached at the intermediate physical entities (IPEs) in the network and retrieved through names. However, the unpredictability of users, IPEs, copy holders, and publishers makes the network suffer from malicious-request attack and data-poisoning attack. To inhibit such attacks, we design a trust model, namely a suspension-chain model (SCM), which is a trust chain that seamlessly merges certificate authority (CA)-based trust and neighbor-based trust. Based on SCM, we propose a data-centric authentication mechanism (DCAuth) integrating certificate collection and packet forwarding, where the suspension certificate chain can be constructed for any authentication to the unpredictable users/IPEs/publishers without accessing servers. We conduct simulations to evaluate the performance of the DCAuth, which show that malicious-request and data-poisoning attacks can be efficiently prevented by the DCAuth.
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning

This paper proposes a new 3D Human Action Recognition system as a two-phase system: (1) Deep Metric Learning Module which learns a similarity metric between two 3D joint sequences using Siamese-LSTM networks; (2) A Multiclass Classification Module that uses the output of the first module to produce the final recognition output. This model has several advantages: the first module is trained with a larger set of data because it uses many combinations of sequence pairs.Our deep metric learning module can also be trained independently of the datasets, which makes our system modular and generalizable. We tested the proposed system on standard and newly introduced datasets that showed us that initial results are promising. We will continue developing this system by adding more sophisticated LSTM blocks and by cross-training between different datasets.
V-CNN: When Convolutional Neural Network encounters Data Visualization

In recent years, deep learning poses a deep technical revolution in almost every field and attracts great attentions from industry and academia. Especially, the convolutional neural network (CNN), one representative model of deep learning, achieves great successes in computer vision and natural language processing. However, simply or blindly applying CNN to the other fields results in lower training effects or makes it quite difficult to adjust the model parameters. In this poster, we propose a general methodology named V-CNN by introducing data visualizing for CNN. V-CNN introduces a data visualization model prior to CNN modeling to make sure the data after processing is fit for the features of images as well as CNN modeling. We apply V-CNN to the network intrusion detection problem based on a famous practical dataset: AWID. Simulation results confirm V-CNN significantly outperforms other studies and the recall rate of each invasion category is more than 99.8%.
Disruption management model for VRPSDP with new customer request for pickup

In order to handle the disruption caused by new customer request for pickup to the original plan of vehicle routing problem with simultaneous delivery and pickup (VRPSDP), this paper conducted the disruption identification analysis on the influence of the disruption event on the original logistics distribution vehicle routing scheme from two aspects of cost and service time based on the disruption management idea. Therefore, the disruption management model for VRPSDP with new customers request for pickup was established. Then, a new dispatching method using tabu search algorithm was designed on the basis of the results of disruption identification. The randomly selected 6 samples from Solomon standard test verify the effectiveness of the model and algorithm. Experimental results show that the disruption management method given by this paper was better than the disruption management schedule of VRPB or traditional adding vehicle. And it could generate a satisfactory distribution vehicle schedule adjustment plan in a short time.
On the crowdsourcing-based radio map construction with noisy location labels

In order to reduce the overheads of constructing a dense radio map as well as to prevent the accuracy degradation, various crowdsourcing-based methods have been developed to automatically collect WiFi RSS measurements to build the radio map. Unlike existing studies focusing on crowdsourcing techniques, this paper deals with how to efficiently and accurately produce the radio map based on crowdsourcing RSS measurements which suffer from noisy location labels. In the literature, gaussian process regression (GPR) is commonly adopted to construct radio maps by sufficiently making use of the spatial correlation among received signal strength (RSS) measurements at nearby locations. However, the standard GPR does not take into account the uncertainties in the location labels attached to the crowdsourcing RSS measurements, which consequently deteriorates the performance of localization systems relying on the corresponding radio maps. Hence, the standard GPR is extended to mitigate the influences of noisy location labels. Experiments are carried out based on practical RSS measurements, and confirm the feasibility and superiority of the proposed method.
Sensor data collection and analytics with thingsboard and spark streaming

The diffusion of small low cost sensors has opened new opportunities for the design of real-time monitoring systems in several application fields. Internet of Things (IoT) is a new branch of Information Technology that connects several of these heterogeneous devices through different network protocols to provide large scale interoperability. In this paper we describe how some open source software tools can be integrated to collect, monitor and process streams of data received in real-time by sensor devices. The tools we have employed are the Things-Board platform to collect sensor data and the Spark Streaming framework for cluster computing to perform data analytics. The presented architecture can be exploited in various monitoring scenarios (such as clinical, environmental or energetic processes) to study the trend of some key parameters and also to produce alerts in case of problematic situations. Without loss of generality, we focus on biomedical data about the breath of patients affected by chronic respiratory disease: real-time monitoring of breath parameters enables automatic ventilotherapy and allows also to warn doctors in time in severe cases.
Wireless network virtualization with non-orthogonal multiple access

We study the problem of joint user clustering and resource allocation for wireless network virtualization (WNV) us­ing non-orthogonal multiple access (NOMA). We aim to maximize the weighted total sum-rate while taking into account the isolation constraint of the mobile virtual network operators (MVNOs). To solve the non-convex formulated problem, we decouple it into three subproblems, i.e., user clustering, resource block (RB) allocation and power assignment. We apply the framework of matching game with externalities to solve the user clustering problem while the solutions for RB and power allocation are derived by using the Lagrange dual approach and complementary Geometric programming, respectively. An alternative maximiza­tion algorithm is provided to achieve a suboptimal solution for the original problem. We propose to classify user equipments (UEs) into three classes, i.e., strong, normal and weak UEs and compare our proposed scheme with general NOMA scheme with two UEs per cluster. Simulation results revel a performance gain of 2.5% in terms of throughput. Moreover, the proposed scheme outperforms the traditional OFDMA scheme in terms of throughput and energy efficiency by up to 40% and 58%, respectively.
Research of MIMU/GPS integrated navigation based on improved Kalman filter

Kalman filter algorithm is basically applied to the MIMU/GPS integrated navigation, but there are always accidents such as abnormal observation noise or noise mutation in reality, where may make the state covariance matrix lose symmetric even the emergence of the divergence of the filter with the consequences of leading to lower the accuracy of integrated navigation system. This paper aims to solve this problem using the principle of singular value decomposition. The main concept behind the algorithm is to achieve updating process via singular value decomposition factors of all the matrices consisting of error covariance, process noise covariance and measurement covariance, instead of Cholesky decomposition on the basis of the conventional Kalman filtering algorithm. The simulation experiments show that the robustness of the proposed algorithm based on singular value decomposition is superior to the Kalman filtering and the square-root filtering algorithm, and improves the navigation accuracy effectively.
The Untapped Genetic Reservoir: The Past, Current, and Future Applications of the Wild Soybean (Glycine soja)

There is a considerable demand for crop improvement, especially considering the increasing growth of world population, continuing climatic fluctuations, and rapidly evolving plant pests and pathogens. Crop wild relatives hold great potential in providing beneficial alleles for crop improvement. Wild soybean, Glycine soja (Siebold & Zucc.), the wild ancestor to the domesticated soybean (Glycine max (L.) Merr. ), harbors a high level of genetic variation. Research on G. soja has been largely devoted to understanding the domestication history of the soybean, while little effort has been made to explore its genetic diversity for crop improvement. High genomic diversity and expanded traits make G. soja populations an excellent source for soybean improvement. This review summarizes recent successful research examples of applying wild soybeans in dissecting the genetic basis of various traits, with a focus on abiotic/biotic stress tolerance and resistance. We also discuss the limitations of using G. soja. Perspective future research is proposed, including the application of advanced biotechnology and emerging genomic data to further utilize the wild soybean to counterbalance the rising demand for superior crops. We proposed there is an urgent need for international collaboration on germplasm collection, resource sharing, and conservation. We hope to use the wild soybean as an example to promote the exploration and use of wild resources for crop improvement in order to meet future food requirements.
The algorithm of mining association rules based on the derivative of the graph model of the datasets

This article devote to mining association rules. We are given a large dataset from the database of hospital. Each transaction consists of health examination result of patients. We developed an efficient algorithm that generates association rules between items in the database. The algorithm based on the modeling graph (mograph) of analyzed set and differentiation one. We also present results of applying this algorithm which shows the effectiveness of the algorithm.
Detection of a signal in the SIMO system with spatial correlation of noise

Detection of signals of primary (licensed) user is one of the main tasks of the users' cognitive radio network (CRN). The article proposes a method for estimating the multi-dimensional “cooperative” likelihood ratio in a CRN with SIMO receivers, which makes it possible to increase the probability of detecting the primary user at a fixed probability of false alarm. The method is based on the mathematical apparatus of the copulas.
Bio-signal processing and 2D representation for brain tumor detection using microwave signal analysis

The paper discussed on the bio-signal processing and 2D representation of a near-field radar microwave analysis system using a mono-static ultra-wideband fabric antenna for brain tumor detection. Simulation of a brain tumor was done by developing a brain phantom with tumor using a new composition of agar-agar, sugar and water based on the relative permittivity, ∊r of the real human brain. In the simulation environment, the antenna travelled along planar y-axis and the reflected signals, S11 from the phantom were collected for processing. Five regular window functions and a proposed superposition technique function were applied to determine the reflection point. Following that, the linear interpolation and contrast enhancement technique were applied for a 2-dimensional (2D) representation enhancement.
A paper-based wall-climbing robot enabled by electrostatic adhesion

In this paper, we report a paper-based wall-climbing robot capable of climbing vertical walls of different materials. The robot, made from paper and shape memory alloy (SMA), can be controlled to climb walls under certain combinations of activation patterns of electrostatic adhesion and contraction of the SMA. Electrostatic adhesion is applied using a paper structure with embedded interdigitated electrodes. This structure, fully compatible with the paper-based robot, is able to output strong and reliable adhesion forces (the resultant friction force can be as high as 1.65 N on specific substrates), and can be easily turned on and off using a commercial high-voltage converter. The SMA embedded in the robot is employed to deform the robot body and induce contracting displacements while being activated. The elastic energy stored in the robot body allows it to complete a repeatable actuation cycle by recovering the SMA automatically after its contraction. With above structures, we demonstrate the walking and climbing ability of this robot with a locomotion speed of 1 mm/s. The climbing of a vertical wall along both the vertical and horizontal directions is achieved.