Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel Assisted Container Virtualization and Its Application for Containerized CUDA Environment

Container technology has shown its advantages, such as lightweight and low latency, over traditional virtual machines for the applications in edge computing. However, the current container design is not for system virtualization and therefore lacks of many important features. In this paper, we present an enhanced version of Docker, the state-of-art container, to support multiple GPU device drivers. We proposed the idea of kernel assisted virtualization for effective container implementation. More specifically, we added additional modual namespace in host OS so that different containers can utilize its own modules. We demonstrated our implementation on containerized CUDA environment. Experiments show that our enhanced container has close to native performance, but with more flexibility for resource sharing.
Adaptive Control of Nonlinear TRMS Model by Using Gradient Descent Optimizers

This study demonstrates an application of direct gradient descent control for adaptively control of a nonlinear stable system models. The approach is based on utilization of gradient descent optimization techniques for the synthesis of control signals to control a specific plant model. In a former work, gradient descent optimizers were designed by considering a first degree instant input-output relation model assumption of the controlled system and this can allow model-independent adaptive control of a class of plant models that can approximate to first order stable plant dynamics. The current study is an extension of this scheme for the purpose of nonlinear adaptive control. Here, we consider a higher degree polynomial assumption of instant input-output relations of the controlled system to obtain gradient descent optimizers that can be applied for adaptive control of a class of nonlinear systems. For evaluation of control performance of gradient descent optimizers, it is applied for the control of nonlinear TRMS model and the results are compared with performance of conventional PID control.
Testing of Autonomous High Precision Panel Assembly Process

In this paper, we present the testing of a novel panel fitting process. This process is based on least-square best-fitting, polynomial multi-regression based statistical estimation, and Greiner-Hormann polygon clipping quality checking methods, to ensure the gap and flush consistency in the final assembly. We focus on the multi-perception vision system, that consists of eight structured light sensors and a mechanical geometrical gripper. Verification and Validation of best-fitting method shows that: the maximum translation error in X and Z coordinate is less than 0.5 mm, in Y coordinate it is less than 2.5 mm, maximum rotation error in three coordinates is less than 0.2°, and it takes just 1.5 seconds to complete the process. The best-fitting method is robust, and meets the process requirements. Verification and Validation of estimation method, that serves as back-up support, shows that: with 3 iterations the translation and rotation errors in three coordinates are 2 mm and less than 2.5° respectively, and it takes 14.3 seconds to complete the process. However, it cannot meet the lead time requirement. This delay is due to the settling time of the prototype gripper between each iteration that is necessary to accommodate the prototype gripper inertia. A final geometric gripper with fixtures for the sensor will resolve this problem by introducing rigidity to the system. Overall, the propose process is a viable and practical solution for the panel fitting process.
Analysis of hot regions prediction in PPI with different amino acid mutation using machine learning algorithm

Discovering hot regions in protein–protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort, meanwhile, different amino acid mutations will bring different energy changes, which lead to different features selection in predictive models; thus, the analysis of predictive models with different amino acid mutation using machine learning algorithm can be very helpful. In this paper, firstly 20 datasets are obtained according to all 20 kinds of amino acid using mutation data from the SKEMPI; then predictive models by combining feature-based classification and density-based incremental clustering were applied in datasets separately. Experiment results show that RctASA, Hydrophobicity, BpASA and BminCX are the best features to discriminate hot spots and non-hot spots at all the datasets according to different kinds of amino acid mutations, and the dataset with LEU, GLY and PRO mutations have the better prediction performance than the other amino acid mutations. These analyses help us to achieve a better insight on protein and their interactions.
Earth Pressure Multipoint Prediction for EPS Shield Based on Multi-Model Ensemble

The tunnel face stability is greatly influenced by the earth pressure in the chamber. It is of great practical significance to improve the performance of the earth pressure multipoint predictive model. In this study, a multi-model ensemble approach based on Lasso, Support Vector Regression, Random Forest and Gradient Boosting Decision Tree is presented for earth pressure multipoint prediction in EPB shield. The Leave-One-Out is adopted to validate the predictive performance. The feature importance is provided by the Lasso, Random Forest and Gradient Boosting Decision Tree model. The experimental results show that the performance of the multi-model ensemble is better than all single model.
Diagnosis of Rotor Winding Inter-Turn Short Circuit Fault in Wind Turbine Based on DFIG Using the TSA-CSA Method

In this paper, a faulty diagnosis of rotor inter turn short circuit in Doubly Fed Induction Generators (DFIG) is established. The aim is to enhance the monitoring system of the wind turbine and the downtime of the DFIG. To achieve this goal, an efficient strategy based on the association of the Time Synchronous Averaging (TSA) technique and Current Signature Analysis (CSA) is developed. The proposed condition monitoring has been examined using simulations built in MatLab SIMULINK environment. Various scenarios have been conducted for a healthy operation and a defective case at different rotor defect levels. The simulation results prove that the elaborated method is practical and reliable, because of its simple stator current measure.
Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis

Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users’ seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.
Road Damage Detection and Classification with Faster R-CNN

This technical paper presents the method that we use in the Road Damage Detection and Classification Challenge, which is designed to detect damages contained in road images photographed by a vehicle-mounted smartphone. In this task, we apply Faster R-CNN to detect and classify damaged roads. Through analyses of aspect ratios and sizes of the damaged areas in the training dataset, we adjust relevant parameters of the model. In order to solve the problem of unbalanced data distribution of different classes, we introduce some data augmentation techniques (contrast transformation, brightness adjustment, and Gaussian blur) before training. Experimental results demonstrate that our method can achieve a Mean F1-Score of 0.6255 in the competition. The source code and model are publicly available at https://github.com/zhezheey/tf-faster-rcnn-rddc.
A Recurrent RBF Neural Network Based on Modified Gravitational Search Algorithm

Recently, Gravitational search algorithm (GSA) was considered as one method for optimizing functions and solving real problems. For the sake of better adjust the values of recurrent RBF neural network (RRBFNN) to make the network achieve better performance, the MGSA is essential in this article. The advised work achieves a better compromise between exploration and development. At the same time, by increasing the guidance of the global optimal particle, the problem that the gravitational search algorithm converges slowly in the later iteration is solved. The Experiment found that the network has better convergence speed and better test accuracy than the RRBFN optimized by the conventional optimization algorithm.
HNGRNMF: Heterogeneous Network-based Graph Regularized Nonnegative Matrix Factorization for predicting events of microbe-disease associations

Identifying the microbe-disease associations provides a great insight into the mechanism that microbes cause diseases at the molecular level. Existing computational methods utilized known microbe-disease associations, microbe features and disease features to predict novel associations. However, there exist different events related to microbe-disease associations, for example, the species abundance of a microbe in bodies will increase or decrease when patients have a disease. In this paper, we propose a heterogeneous network-based graph regularized nonnegative matrix factorization (HNGRNMF) to predict microbe-disease associations as well as the events. HNGRNMF constructs a heterogeneous network based on observed microbe-disease association-related events: increase/decrease on species abundance of microbes, and extracts two event matrices from the heterogeneous network, and then decomposes matrices into the common low-rank disease space and two different low-rank microbe spaces under the frame of nonnegative matrix factorization. In addition, microbe-microbe similarities and disease-disease similarities are integrated, respectively, and incorporated into HNGRNMF. The experiment results demonstrate that HNGRNMF accurately predicts microbe-disease associations and their events when evaluated by leave-one-out cross validation and 5-fold cross validation.
Using deep neural network to recognize mutation entities in biomedical literature

Automatic recognizing mutation mentions plays a fundamental and critical role in extracting variant-disease relation from biomedical literature. In this paper, we proposed an advanced model for mutation mentions detection by using deep network in combination with decoding algorithm and regular expression. Inspired by the distributed representation of words and characters, we divide each word by letters of difference case, numbers and special characters into tokens for training a token embedding which can capture some nomenclature features of mutations. To build the network, we implemented Bi-directional LSTM (long short-term memory) layers to learn a general form of mutation mentions while capture long-term context information and fully-connected layers to improve the fitting capability, using concatenation of word vectors training from token embeddings as the input. Viterbi algorithm was used to decode the previous output to access initial labeled sequence. On top of that, regular expression patterns were used to label the mutation mentions, which provided extra information to optimize the initial output. While training and testing on NCBI tmVar mutation corpus, our model achieved F-score of 91.59% which performed better than current reported systems.
A Layered Communication Optimization Method Based on OpenFOAM

Since Computational Fluid Dynamics (CFD) has emerged as an interdisciplinary science between different subjects, numerous CFD software are increasingly developed on typically layered frameworks. In the literature, only a limited number of optimizations are based on the framework. This paper reveals a general picture of multiphase flow simulation in OpenFOAM and proposes a layered communication optimization method. There are three layers: physical modeling layer, numerical solving layer and parallel computing supporting layer. In these three layers, we respectively eliminate redundant communications in a key module called Multidimensional Universal Limiter for Explicit Solution (MULES), decrease global communications by rearranging preconditioned conjugate gradient (PCG) solver and add non-blocking reduction call interface. Experiments on high-performance computing platforms show that our optimization dramatically enhances the strong scalability of OpenFOAM, increasing the optimal process number by up to three times in both dam-Break flow case and complex 3D head-form case. With a higher degree of parallelism, the shortest execution time, therefore, decreases by 38.16% in the former case and by up to 63.87% in the latter one.
Key based Deep Data Locality on Hadoop

Apache Hadoop is a famous framework for big data science. Most of the research for improving the speed of big data analysis is researching based on Hadoop modules such as Hadoop common, Hadoop Distribute File System (HDFS), Hadoop Yet Another Resource Negotiator (YARN) and Hadoop MapReduce. The paper focuses on data locality on HDFS and MapReduce to improve the performance. The input data is divided into several blocks and stored in HDFS. Each block has sever key-value fair in map stages. The paper use the keys in block to make key-based Deep Data Locality (DDL). The MapReduce with key-based DDL reduce some steps on map stage, shuffle stage, and reducer stages to improve the performance of MapReduce. We tested the performance of MapReduce with block-based DDL and key-based DDL to compare with default MapReduce. According to the test, MapReduce with key-based DDL is 28% faster than default MapReduce and 15.4 % faster than MapReduce with block-based DDL. Additionally, key-based DDL can be combined other data locality methods to improve the Hadoop. Combined key-based DDL and block-based DDL improve the Hadoop performance up to 52.5%. The paper also introduced the simulator for testing the performance of MapReduce with applied data locality methods on Hadoop. The simulator display a performance of each stage of MapReduce using graph. Key-based DDL can be combined with other data locality research to get optimized performance in various data types and node’ status in the simulator.
A Comparative Study of Machine Learning Algorithms for Financial Data Prediction

Machine learning has been widely used as a part of stock market investment strategies, whether for forecasting exchange rate or market volatility, and also in different classification problems in order to help for decision making. In this paper, we work on Brazilian and Chilean currency exchange data using two different algorithms. These experiments are based on 15 years historical data, the predictions are made for the next day price of the action. In addition, of historical data, our dataset includes ten technical indicators as inputs of each prediction model. Our contribution is a comparison of neural networks algorithms results with a multi linear regression algorithms optimized with particle swarm Metaheuristic.
High-Throughput Adaptive Data Virtualization via Context-Aware Query Routing

Query throughput degradation is a common problem when migrating applications from an on-premises to a cloud-based data warehouse. In this paper, we propose CAQR: a context-aware query router. CAQR is a middleware-based database replication solution that improves workload throughput by combining the strong points of existing data replication methods and mitigating their limitations at the same time. CAQR provides strong data consistency without the need for real-time synchronization or manual annotation of queries. Employing a master-slave architecture, CAQR runs atop an adaptive data virtualization layer and automatically sends a query to the correct replica, so that the execution preserves the strong data consistency property. Extensive experiments on real-world and benchmark workloads show the practicality and efficiency of the solution: CAQR achieves near-linear scalability, without requiring any changes to the application layer.
Meteorological Feature Selection Method Based on Information Value and Maximum Correlation

Accurate numerical weather forecast is the key to the safe operation of high altitude airports. In order to solve the problem of information redundancy of massive observation data in meteorological forecast, a feature selection method based on Information Value(IV) and Maximum Correlation(MC) is proposed in this paper. Firstly, the observation data and its various statistical features are taken as the original feature set, and the IV of each dimension is calculated and sorted accordingly. Then, the dependence of various weather conditions on the temporal characteristics of the observed data is analyzed, and the maximum statistical correlation is calculated, so as to determine the feature dimension that needs to be retained. Finally, the feature selection is carried out from the original feature set according to the reserved dimension and feature priority. Based on the real observation data of high altitude airport, in this paper, three types of classifiers are used, namely Support Vector Machine (SVM), Decision Tree (DT) and eXtreme Gradient Boosting (XGBoost), to conduct experiments of extreme weather prediction on feature sets that extracted from information value, gradient boosting tree and correlation coefficient. The results show that SVM cannot converge by using the original features or sets obtained by the gradient ascending tree. Compared with the correlation coefficient method, the classification performance of SVM represented by Fl-Score is improved by about 40 percentage using method in this paper. Therefore, the method proposed in this paper can effectively improve the classification accuracy of weather forecasting, and reduce the required characteristic dimensions and data volume for model construction.
Real-Time Object Detection Application for Visually Impaired People: Third Eye

Visually impaired people can’t move safely outdoors because they can not perceive the outside obstacles as normal people. The prototype application in this study aims to make the visually impaired people’s lives easier with the mobile devices. The mobile application with the designed chest apparatus will be like a virtual third eye that is not too costly and easily accessible. All the design and codes is shared with GPL free software licence. The application is developed for the Android platform. Image processing and machine learning technologies are used. Methods, design, and findings are discussed.
Initialization of Membership Degree Matrix for Fast Convergence of Fuzzy C-Means Clustering

In fuzzy clustering, initialization of cluster centers and membership degree matrices is one of the mandatory tasks for starting the fuzzy partitioning algorithms. Quality initialization of these matrices provides fast convergence to the final cluster centers. This reduces the required number of iterations and consequently results with low computational costs. In this paper, a novel algorithm is proposed to generate initial membership degree matrix for starting fuzzy partitioning clustering algorithms such as Fuzzy C-means, Possibilistic Fuzzy C-means and other variants of these algorithms. The proposed initialization algorithm is based on the selection of the feature with the highest variability among the others in a multidimensional numeric dataset. The experimental results show that the proposed algorithm performs well in reducing the number of iterations needed to converge to the final cluster centers for the examined synthetic and real datasets.
Clinical Text Classification with Word Embedding Features vs. Bag-of-Words Features

Word embedding motivated by deep learning have shown promising results over traditional bag-of-words features for natural language processing. When trained on large text corpora, word embedding methods such as word2vec and doc2vec methods have the advantage of learning from unlabeled data and reduce the dimension of the feature space. In this study, we experimented with word2vec and doc2vec features for a set of clinical text classification tasks and compared the results with using the traditional bag-of-words (BOW) features. The study showed that the word2vec features performed better than the BOW-1-gram features. However, when 2-grams were added to BOW, comparison results were mixed.
Survey on Physical Layer Security in Optical Wireless Communication Systems

As the existing radio-frequency (RF) networks cannot meet the ever-increasing data rate demand, the optical wireless communication (OWC), which uses a wide untapped unregulated spectrum, has been proposed as a promising technology to overcome the RF spectrum limitations. On the other hand, with the increasing demand for high data rate and the prevalence of the broadcast-nature networks, researchers have recently come up with new mechanisms to improve secure communication using physical layer techniques. Compared to RF networks, the OWC networks are more secure and less susceptible to the interception because of the small coverage provided by LEDs, and because they work properly only in the presence of the line-of-sight (LoS) components. However, the security in OWC networks is still an issue, especially in visible light communication (VLC) when the transmitted information can be accessed by multiple users as in public areas, meeting rooms, laboratories, and libraries. That means potential eavesdroppers may be existing to gather confidential messages. This paper reviews all the conducted work on physical layer security (PLS) in two types of OWC networks, which are the VLC and free space optical (FSO) networks. Furthermore, the paper proposes several open problems in these networks to optimize and enhance the security performance.
MPTCP Throughput Enhancement by Q-Learning for Mobile Devices

Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.