Miracle-dz/UniFAR-FTUs huggingface dataset:
A robotic proximal sensing platform for in-field high-throughput crop phenotyping

A rapidly increasing world population and changing climate means plant scientists will need to be able to efficiently develop crop varieties to feed the world. Although the technology for sequencing the genomes of plants has advanced, the technology for characterizing the physical traits of plants has remained relatively static. This gap in technology has become known as the “Phenotyping Bottleneck. ” To close this gap, researchers are working to develop robotic systems that can efficiently recognize various physical traits of plants. The goal of this research was to investigate how the design requirements for a vehicle that interacts with a biological system are translated into a working mechanical system. The design requirements include the ability to traverse and image crops in 30-inch wide space between crop rows. This thesis reports the concepts, design decisions, and manufacturing process around the construction of such as a phenotyping robot namely Phenobot 3.0, which stands for the 3 generation of our phenotyping robot series. Phenobot 3.0 is optimized for phenotyping maize plants in the field but can be adapted for phenotyping other crops. Specifically, Phenobot 3.0 is designed to be narrow to fit between the rows but also tall for sensor placement so that it can gather data from the emergence to the full height of maize plants. To achieve the needed stability of the sensors (LiDAR, stereo cameras), the robot employs a selfleveling mast to cope with the uneven terrain while ensuring proper sensor to plant placement. Unlike many other field-based phenotyping robots, Phenobot 3.0 employs a 4wheel-drive articulated drivetrain that has differentials on each pair of wheels to ensure maximum steering efficiency and prolonged operational time in the field. Phenobot 3.0 will be a member of PhenoNet, a network of five robots for maize plant phenotyping
Bayesian Calibration and Inference for Multiple Machines

In this thesis, simulation models, also called simulators, are used to perform predictions on machines. One of the challenges with performing predictions using simulation models, is that they do not fully describe the true process of the machine. Often, there exists a model discrepancy: the difference between the simulator output and the true process. The working hypothesis is that, by using multiple machines with some parameters and hyperparameters in common, it is possible that the model learns from all the machines, and perform better predictions. There are three different models used to perform inference. The first model, is a model from Brynjarsdóttir and O’Hagan (2014), slightly modified, with individual parameters and an individual discrepancy term. It is referred to as the individual model. Two new model frameworks are introduced in this thesis, referred to as Model 1 and Model 2. Model 1 assumes common parameters for the machines and has an individual discrepancy term. Model 2 assumes common parameters for the machines, and has a common discrepancy term and an individual discrepancy term. All three models are Latent Gaussian models, with their discrepancy terms following Gaussian fields with Matérn covariance functions. The last two models evaluates the multiple machines simultaneously, and are used to create observations, resulting in two new types of machines, referred to as the Ideal machines 1 and the Ideal machines 2 respectively. These ideal machines are created to learn more about the predictive perfomances of Model 1 and Model 2. Additionally, the three models are applied on multiple simple machines, the same machine described by Brynjarsdóttir and O’Hagan (2014). There are four types of prediction points that are tested: interpolation points, extrapolation points, pseudo extrapolation points (prediction points that are extrapolation points for some machines and interpolation points for others) and prediction points that are located far from the observations. Different designs of where the observations created are located, are used to test the models predictive performance on the four types of prediction points. The prediction is performed using the R-package INLA, which is less computationally expensive than using Markov chain Monte Carlo algorithms. For all four types of prediction points, evaluating the machines simultaneously gives better results than evaluating the machines individually. Model 2 has the best predictive performance for all three machines and all four types of prediction points. Model 1 has equally good predictive performance for the Ideal machines 1 as Model 2, and in general better predictive performance than the individual model. There is not one type of prediction point for any of the multiple machines, where the individual model has the best performance. The results are found using only synthetical machines and only when the calibration parameter has the same value for all machines. Suggestions for further research, are to use real machines or to vary the value of the calibration parameter for all machines.
Thyroid image classification algorithm using DT CWT

The following paper presents an algorithm for thyroid image classification. To design it we used the Dual-Tree Complex Wavelet Transform for image feature extraction and Hausdorff distance to compute similarity distance between the feature vector of the query-image and all the image feature vectors stored in the image database. Then, we performed experiments to estimate the effectiveness of the proposed algorithm which showed high values of precision.The following paper presents an algorithm for thyroid image classification. To design it we used the Dual-Tree Complex Wavelet Transform for image feature extraction and Hausdorff distance to compute similarity distance between the feature vector of the query-image and all the image feature vectors stored in the image database. Then, we performed experiments to estimate the effectiveness of the proposed algorithm which showed high values of precision.
Backstepping Control and Transformation of Multi-Input Multi-Output Affine Nonlinear Systems into a Strict Feedback Form

This dissertation presents an improved method for controlling multi-input multioutput affine nonlinear systems. A method based on Lie derivatives of the system’s outputs is proposed to transform the system into an equivalent strict feedback form. This enables using backstepping control approaches based on Lyapunov stability and integrator backstepping theory to be applied. The geometrical coordinate transformation of multi-input multioutput affine nonlinear systems into strict feedback form has not been detailed in previous publications. In this research, a new approach is presented that extends the transformation process of single-input single-output nonlinear. A general algorithm of the transformation process is formulated. The research will consider square feedback linearizable multi-input multi-output systems where the number of inputs equals to the number of outputs. The preliminary mathematical tools, necessary and sufficient feedback linearizability conditions, as well as a step-by-step transformation process is explained in this research. The approach is applied to the Western Electricity Coordinating Council (WECC) 3-machine nonlinear power system model. Detailed simulation results indicate that the proposed design method is effective in stabilizing the WECC power system when subjected to large disturbances. ©2019 Khalid Salim D. Alharbi All Rights Reserved
China Family Loss Prediction Research Based on Machine Learning

Social change has a direct influence on the family loss. To this end, we propose a novel approach to model the probability of family loss by learning the large-scale real-world survey data from China Family Panel Studies (CFPS). Especially, combining individual economic, health, identity, family profile and other information, we develop the scalable tree boosting model named Family Loss Prediction Model (FLPM) to model the family loss. Extensive experiments on real-world survey data validate the effectiveness of FLPM for measuring the probability of family loss in China. Furthermore, compared with the XGBoost and GBDT models, the experimental results show that the FLPM model is better and the precision is 98.48%, which provides an important basis for this study.
An improved clinical data similarity algorithm based on ICD10

Disease prediction helps prevent disease and early diagnosis, and accurate classification of patients greatly improves the accuracy of disease prediction. Today’s massive multi-dimensional medical data and its similarity algorithms provide the basis for the classification of clinical diseases. Based on this, we randomly generated the simulated clinical data of ICD10 structure, used the improved similarity algorithm to calculate the similarity and classification of the two patients, and found the patients belonging to different disease categories in the classified patient group. This finding provides a scientific basis for the correction of genetic algorithms and genetic research.
An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization

ABSTRACT Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimization techniques are not able. To overcome the limitation of the classical approach, in the recent study, a large number of metaheuristic methods have been investigated to improve the solution quality concerning convergence and accuracy on the complex problems. Nowadays, a popular metaheuristic algorithm is introduced called teaching–learning-based optimization (TLBO). It is recently being used as an innovative, and robust method to solve the global optimization problem, inspired by the teaching–learning phenomenon. On the other hand, particle swarm optimization (PSO) algorithm is one of the most utilized algorithms in the current scenario, which has indicated acceptable efficiency. Genetic algorithm (GA) is a valuable component of metaheuristic which has been applied in various research applications. In this article, the performance of TLBO algorithm is estimated on 25 numerical test suites against other metaheuristic algorithms such as PSO and GA. The results of our experimental study show that TLBO outperforms the PSO and GA algorithm regarding convergence solution.
MP-RDMA: Enabling RDMA With Multi-Path Transport in Datacenters

RDMA is becoming prevalent because of its low latency, high throughput and low CPU overhead. However, in current datacenters, RDMA remains a single path transport which is prone to failures and falls short to utilize the rich parallel network paths. Unlike previous multi-path approaches, which mainly focus on TCP, this paper presents a multi-path transport for RDMA, i.e. MP-RDMA, which efficiently utilizes the rich network paths in datacenters. MP-RDMA employs three novel techniques to address the challenge of limited RDMA NICs on-chip memory size: 1) a multi-path ACK-clocking mechanism to distribute traffic in a congestion-aware manner without incurring per-path states; 2) an out-of-order aware path selection mechanism to control the level of out-of-order delivered packets, thus minimizes the meta data required to them; 3) a synchronise mechanism to ensure in-order memory update whenever needed. With all these techniques, MP-RDMA only adds 66B to each connection state compared to single-path RDMA. Our evaluation with an FPGA-based prototype demonstrates that compared with single-path RDMA, MP-RDMA can significantly improve the robustness under failures ( $2\times \sim 4\times $ higher throughput under 0.5%~10% link loss ratio) and improve the overall network utilization by up to 47%.
OpenGL 3D crowd evacuation simulation at universiti tun hussein onn malaysia (UTHM) hostel

Crowd simulation is the process of simulating characterized agents or entities using computer application to analyse it in virtual scene or virtual environment. This paper investigates the best route path for agents to act in avoiding the fire hazards with different designated type of stairs in shop lots that were converted to hostel dormitory for students. 3D social force agent’s model and 3D fire hazards were designed in Microsoft Visual Studio C++ software and OpenGL library. A research was conducted using social force model behaviour and were taken by 10 and 15 agents to analyse the time taken to complete the evacuation process. The acceleration produced where it is related with route path taken by agents, interaction forces of agents and interaction forces of wall are the main research system to analyse agents’ behaviour during simulation. Different simulations have been used to determine the best and fastest route taken by agents. In summary, the lower the number of agents, the lower the time allocated by agents to complete the evacuation. Finally, less number of agents using the designated straight stairs gave a lower time to complete evacuation process and reached high level of security to avoid being exposed to fire hazards.
Modern Optimization for Statistics and Learning

Traditional problems in statistics and machine learning are relatively well understood – they often feature low dimensionality, convex loss functions, and independent, identically distributed data. By contrast, many modern learning problems feature high dimensional data, non-convex learning objectives, and data distributions that change during the learning process. Whether the problem of interest is labeled as statistics, machine learning, statistical learning, or reinforcement learning, methods for solving it can be viewed as the stochastic optimization of some objective function. Accordingly, we address the aforementioned challenges via the lens of statistical optimization – a statistical approach for understanding and solving stochastic optimization. In particular, we focus on deriving new methodology with computational and statistical guarantees for two classes of problems: recovering and performing inference on latent patterns in high-dimensional graphical models, and continuous control over bounded action spaces. In the first part of this dissertation, we consider a class of cluster-based graphical models. We introduce a novel algorithm for variable clustering named FORCE, based on solving a convex relaxation of the K-means criterion, as well as post-dimension reduction inferential procedures. In the second part, we consider the reinforcement learning (RL) setting, where an agent seeks to learn a decision-making policy based on feedback from its environment. We derive a novel class of variance-reduced estimators called Marginal Policy Gradients, and demonstrate both their improved statistical properties and their application to several control tasks.
EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks

Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models.
Increasing Port Performance through Port Navigation Safety Assessment using the Formal Safety Assessment Method (Case Study Port of Tanjung Priok - Indonesia)

The study is aimed to assess the navigation safety in the port surrounding the area as part of the Indonesian Government�s efforts to increase the port performance and minimize the cost of maritime logistics. The port navigation system and infrastructure such as vessels traffic management system, navigation lanes, port basin, quay, etc. play an important role in determining the productivity and efficiency of the port, especially in the busy port such as Tanjung Priok, Indonesia. Any accident would interrupt port activities and creates unnecessary costs. Formal Safety Assessment method, as introduced by the International Maritime Organization was implemented in the study. The assessment was carried out using qualitative and quantitative risks analyses, based on the available data and information gathered from various sources. It was identified that accidents occurred in the port waterfront were mainly due to the ships, environment, human factors, and management. Results of the assessment were then used as the basis for proposing an improvement strategy.
Multi-Grained-Attention Gated Convolutional Neural Networks for Sentence Classification

The classification task of sentences is very challenging because of the limited contextual information they contain. Current methods based on Convolutional Neural Networks (CNNs) for sentence classification do not have a simple and effective mechanism to determine which features in the network are critical and could become large enough that affect the classification result. In this paper, we propose a new CNN model named Multi-Grained-Attention Gated CNN (MGAGCNN), which generates attention weights from the context window of different sizes based on the MultiGrained-Attention (MGA) gating mechanism. Through the experiments of comparison and attention visualization, we demonstrate that with the help of MGA gating mechanism, our model could learn more meaningful and comprehensive abstract features and adequately identify the critical features, and enhance their influence on the prediction of sentence category. In addition, we propose an activation function named Natural Logarithm rescaled Rectified Linear Unit (NLReLU). Experimental results show that our model could achieve a general accuracy improvement ranging from 0.4% to 1.7% (compared with standard CNN models), and gain competitive results over the strong baseline methods on four out of the six tasks, and NLReLU could achieve comparable performances on MGAGCNN to other well-known activation functions.
Efficient detection of false data injection attack with invertible automatic encoder and long-short-term memory

The false data injection attack can tamper with the measurement information collected by the Supervisory Control and Data Acquisition, threat the security of state estimation. Therefore, the analysing methods and detection methods of false data injection attacks have important theoretical and practical significance for ensuring the safe operation of smart grids. This study uses the invertible automatic encoder to reduce the dimension of the original data and uses the long-short-term memory to detect false data injection attacks. This method overcomes the shortcomings of shallow algorithm and traditional machine learning algorithm for power big data training and avoiding the problems of gradient explosion and gradient disappearing during training. Finally, the authors perform a large number of experiments in the IEEE 118-node test system and the 300-node test system and verify the effectiveness of the proposed method.
Physiological Brain Monitoring

Several techniques are available for global and regional brain monitoring that provide an assessment of cerebral perfusion, oxygenation, and metabolic status, and early warning of impending brain hypoxia and ischemia (Supplemental Digital Content 1, http:// links.lww.com/ASA/A397). Some monitoring modalities are well established, whereas others are relatively new to the clinical arena and their indications are still being evaluated; all have advantages and disadvantages (Table 1). The general indications for intracranial monitoring are summarized in Table 2. Monitoring of several physiological variables simultaneously (multimodal monitoring) has enabled a move away from rigid physiological target setting to an individually tailored, patient-specific approach toward management after acute brain injury. The pathophysiology of acute brain injury is complex and involves the interrelation between changes in cerebral blood flow (CBF), cerebral oxygen and glucose delivery and utilization, and electrophysiological derangements, with substantial regional and temporal heterogeneity. Although intracranial pressure (ICP) and cerebral perfusion pressure (CPP) are crucially important and are routinely monitored, they do not provide an assessment of the adequacy of cerebral perfusion. Therapeutic targets and choice of therapy are therefore best determined by multimodal monitoring.
Fecal Microbiota Transplantation for Treatment of Severe, Recurrent, and Refractory Clostridium difficile Infection in a Severely Immunocompromised Patient

AbstractInformation on the safety of fecal microbiota transplantation (FMT) among immunocompromised patients with Clostridium difficile infection (CDI) is limited, and some have recommended avoiding FMT in this population. We report a case of severe, refractory CDI in an orthotopic heart transplant
NJM-Vis : applying and interpreting neural network joint models in natural language processing applications

Neural joint models have been shown to outperform non-joint models on several NLP and Vision tasks and constitute a thriving area of research in AI and ML. Although several researchers have worked on enhancing the interpretability of singletask neural models, in this thesis we present what is, to the best of our knowledge, the first interface to support the interpretation of results produced by joint models, focusing in particular on NLP settings. Our interface is intended to enhance interpretability of these models for both NLP practitioners and domain experts (e.g., linguists).
Improving Physician Assistant Students’ Competencies in Developmental Disabilities Using Virtual Patient Modules

&NA; Two multimedia, virtual‐patient modules that focused on patients with developmental disabilities were developed as clinical training tools for physician assistant (PA) students. Primary objectives of the modules included (a) improved knowledge and (b) reduced perception of difficulty in treating patients with developmental disabilities. The modules were developed using an iterative, collaborative process within a core development team that included medical professionals, multimedia specialists, parents of children with developmental disabilities, and an individual with a developmental disability. The modules included two virtual patient cases — a well‐child visit for a young adolescent female with Down syndrome, and a routine office visit for an infant born significantly preterm. Within the context of the two cases, students were required to identify appropriate and effective clinician‐patient interactions, in addition to relevant medical and developmental concerns for this population. An effectiveness study utilizing a sample of 42 PA students suggest that the modules are an effective tool for improving PA student knowledge and reducing their perception of difficulty in providing care to patients with developmental disabilities and their families.