Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Strategy for Improving Power Supply Capacity in Distribution Networks with Electric Vehicle Integration

In order to alleviate the heavy air pollution problem in China in recent years, power energy alternative loads, such as electric vehicles, are widely integrated into distribution networks, which has a significant impact on power supply capability of distribution networks. In this paper, considering the randomness of electric vehicles, probabilistic multiple scenarios generation method is proposed based on Latin hypercube sampling method, Cholesky decomposition and synchronous back-off reduction, realizing the quick scenario generation with the balance of scenario number and precision. Based on probabilistic multi-scene, taking the power supply ability of distribution network as the main objective, a robust operation optimization model of distribution network containing electric vehicles is established in order to optimize the opening and closing status of the switches. Ant colony algorithm based on the Pareto optimal is adopted to solve the model. A modified IEEE 33 node system is used to verify the effectiveness of the model and algorithm, and simulation results show that the proposed method can effectively promote the power supply ability of distribution networks, and can greatly improve the computational efficiency of the model in the case that lots of uncertain parameters exist due to large-scale integration of electric vehicles.
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the improvement of administrations figuring and distributed computing, it has turned out to be conceivable to outsource extensive databases to database specialist co-ops and let the suppliers keep up the range-inquiry benefit. Nonetheless, a few information may be touchy that the information proprietor does not have any desire to move to the cloud unless the information classification and inquiry security are ensured. We propose the Random Space Encryption (RASP) approach that permits productive range look with more grounded assault versatility than existing proficiency centered methodologies. The arbitrary space irritation (RASP) information annoyance technique to give secure and proficient range question and kNN inquiry administrations for ensured information in the cloud. The RASP information annoyance strategy consolidates arrange protecting encryption, dimensionality development, arbitrary commotion infusion, and irregular projection, to give solid flexibility to assaults on the irritated information and questions. It likewise saves multidimensional reaches, which enables existing ordering systems to be connected to speedup extend question handling. The kNN-R calculation is intended to work with the RASP go inquiry calculation to process the kNNinquiries.
A new prediction model of solar radiation based on the neuro-fuzzy model

ABSTRACT In this work, we proposed a new prediction model-based neural network (NN) and neuro-fuzzy systems. The mentioned model is applied over the solar radiation signal which has been used as a new clean energy source recently. The proposed forecasting model consists of two main steps, namely feature selection and forecast engine. In the first step the best candidate inputs are selected and used as input of the next section. The main advantage of the proposed forecasting engine is that there is no need for any past information of the appearances of the input time-series in order to forecast their next values. This model is combined with an intelligent algorithm to improve the abilities of prediction. Efficiency of the proposed method is applied over real-world engineering test cases. The obtained results prove the validity of the proposed model.
Improving the video streaming backend with on-the-fly format conversion and cloud storage

Online advanced media streaming services using HTTP adaptive stream- ing are increasingly popular. However in practice, the multi-protocol, multi- format nature of adaptive streaming creates a lot of engineering eort and costs for the operators, in the storage and preparation of the dierent formats. In this work, we acknowledge these issues and we study a stream- ing setup that can address these. Such streaming setup consist of a backend cloud storage and a processing node that generates streaming presentations for different devices on-the-fly. We analyse the streaming setup and its performance by testing in cloud deployments. Through this evaluation, we identify the performance limitations of the setup, imposed by the transfer of data between the object storage and the processing node. We propose a new backend storage caching scheme, based on rarely used existing feature of dref in the specication of the MPEG-4 standard. Experimental results show that the proposed scheme can improve the streaming performance, such as reduced latency and increased outgoing traffic volume towards the clients
Control and Optimization of Soft Exosuit to Improve the Efficiency of Human Walking

Researchers have strived to develop lower limb wearable devices to improve the efficiency of human walking, however significant device inertia, kinematic constraints and the lack of optimal control strategies has limited performance when testing with human subjects. To alleviate these challenges, we developed the soft exosuit, a textile based wearable device that provides a conformal, unobtrusive and compliant means for interface with the human body. Unlike most rigid exoskeletons, the components of the soft exosuit on the lower extremity can be worn like light clothing. Combined with proximal mounted actuators, soft exosuit can provide forces that are in parallel to the lower limb muscles to improve the efficiency of walking. This thesis details the developments of soft exosuits, actuation platforms, real-time controllers and optimization methods. The working principle of the soft exosuits and the development of multi-joint actuation platforms that can provide real-time controlled assistance are presented. Two types of exosuit controllers, including iterative force-based position control and switching admittance-position control are designed and evaluated. The iterative force-based position control can accurately detect the hip extension onset timing and track peak timing and peak magnitude of the assistive profile within 1% of error. The switching admittance-position controller can track the whole shape of assistive profile with a root-mean-square error of 3.4%. For the exploration of human-robot interaction, human subject studies evaluating the effects of multi-joint assistance and timing of hip extension assistance are conducted. In the
Clustering of multiple instance data.

CLUSTERING OF MULTIPLE INSTANCE DATA Andrew D. Karem April 25, 2019 An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags – this region is referred to as a target concept (TC). Existing methods either only identify a single target concept, do not provide a mechanism for selecting the appropriate number of target concepts, or do not provide a flexible representation for target concept memberships. Thus, they are not suitable to handle data with large intra-class variation. In this dissertation we propose new algorithms that learn multiple target concepts simultaneously. The proposed algorithms combine concepts from data clustering and multiple instance learning. In particular, we propose crisp, fuzzy, and possibilistic variations of the Multi-target concept Diverse Density (MDD) metric, along with
Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming (INLP) formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.
Fuzzy-Rough Classification for Brainprint Authentication

The electroencephalogram (EEG) signal is used as biometric modality, because it is proven to be unique, universal and collectable. This work aims to assess the performance of fuzzy-based techniques for brainprint authentication modelling. We benchmark the performance of Fuzzy-Rough Nearest Neighbour (FRNN) technique to the Discernibility Nearest Neighbour (D-kNN) and the Fuzzy Lattice Reasoning (FLR) techniques using the selected samples of brainwaves’ data from the original UCI EEG dataset. All the three classifiers are available in the fuzzy-rough version of WEKA implementation tool. Selected 9 EEG channels located at the midline and lateral regions were used in the experimentation. The coherence, mean of amplitudes and cross-correlation feature extraction methods were used to extract the EEG signals. The area under ROC curve (AUC) measurement of FRNN was promising against the D-kNN and FLR techniques. The FRNN model has achieved the best performance of AUC measure at 0.904 in opposition to the D-kNN and FLR models, where both recorded 0.770 and 0.563, respectively. However, the classification accuracy shows significantly no difference among the three classifiers. The results confirmed that the classification accuracy of D-kNN and FLR techniques is not reliable, because they are highly contributed by the true negative cases. Hence, we conclude that the FRNN model is less biased to imbalance data problem as compared to the D-kNN and FLR models. Future work of this research should focus on optimizing the EEG channel and feature selection in order to obtain a better data representation of biometric brainprint for more efficient authentication in imbalance data problem.
The State of the Art in Visualizing Multivariate Networks

Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real‐world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state‐of‐the‐art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.
A Convolutional Neural Network for Airport Security Inspection of Dangerous Goods

According to the problems of heavy workload, low efficiency, easy fatigue misjudgement with artificial recognition and imbalance of dangerous goods image dataset in airport security inspection caused the low recognition accuracy, a convolution neural network automatic recognition model based on oversampling for dangerous goods is proposed. Firstly, the oversampling technique is used to equalize the dataset of dangerous goods image, and then the image is inputted into the convolution neural network model composed of four convolution layers and one full-connection layer for training. The stochastic deactivation optimization technique is introduced in the training to get better recognition effect. The experimental results on a dangerous goods image dataset of public security in 2017 show that the recognition accuracy of the model can reach 90.7% after equalization, which is 33.4% higher than that before equalization. In addition, the recognition accuracy of the model is 5.8%, 7.2% and 5.4% higher than that of GoogleLeNet, AlexNet and ResNet respectively. The model has high recognition accuracy and good real-time performance, which is of positive significance to improve the level of airport security intelligence.
Three way decisions based grey incidence analysis clustering approach for panel data and its application

PurposeThis paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.Design/methodology/approachBecause of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.FindingsThe results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.Practical implicationsThe novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.Originality/valueThe proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.
Objective stress monitoring based on wearable sensors in everyday settings

Abstract Monitoring people’s stress levels has become an essential part of behavioural studies for physical and mental illnesses conducted within the biopsychosocial framework. There have been several stress assessment studies in laboratory-based controlled settings. However, the results of these studies do not always translate effectively to an everyday context. The current state of wearable sensor technology allows us to develop systems measuring the physiological signals reflecting stress 24/7 while capturing the context. In this paper, we present a stress monitoring system that provides objective daily stress measurements in everyday settings based on three physiological signals: electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) using Shimmer3 ECG, Shimmer3 GSR+, and Empatica E4 wearable sensors. We perform controlled stress assessment experiments on 17 participants in which we successfully detect stress with a 94.55% accuracy for 10-fold cross-validation and an 85.71% accuracy for subject-wise cross-validation. In everyday settings, the system assesses stress with an 81.82% accuracy. We also examine whether motion artefacts affect stress assessment and filter the low-confidence readings to minimise false alarms.
ONCObc-ST: An Improved Clinical Reasoning Algorithm Based on Select and Test (ST) Algorithm for Diagnosing Breast Cancer

The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain of (medicine as example) application of such algorithms. Most reasoning algorithms for medical diagnosis are either limited by their techniques or accuracy and efficiency. Even the Select and Test (ST) algorithm which is considered a more approximate reasoning algorithm is also limited by its approach of using bipartite graph in modeling domain knowledge and making inference through the use of orthogonal vector projection for estimating likelihood of diagnosis at the clinical decision stage (induction). While the bipartite graph knowledge base lacks n-ary use of predicate on concepts, orthogonal vector projection on the other hand has high computation for the inference process. The aim of this paper is to enhance ST algorithm for improved performance and accuracy. First, we propose the use of ontologies and semantic web based rule for knowledge representation so as to provide support for inference making. Furthermore, three major improvements were added to ST algorithm to aid the improvement of its approximation. Secondly, we designed an inference making procedure to enable interaction with the knowledge base mentioned earlier. Thirdly, we model Hill’s Criteria of Causation into clinical decision stage of ST to overcome the limitation of orthogonal vector projection. Lastly, the improved ST algorithm was largely represented and described using set notations (though implemented as linked-list and queues) and mathematical notations. The result of the improved ST algorithm revealed a sensitivity of 0.81 and 0.89 and specificity of 0.82 and 1.0 in the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. In addition, the accuracy obtained from the proposed algorithm was 86.0% and 88.72% for the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. This enhancement in accuracy was obtained at a slowdown time due to the reasoning process and ontology parsing task added to the enhanced system. However, there was an improvement in the accuracy and inference power of the resulting system.
Hybrid techniques to improve face recognition based on features extraction methods and Haar discrete wavelet transformation

This paper uses hybrid techniques to improve the rate of recognition for a face from identified data set of faces. These techniques are summarized by applying firstly the Haar discrete wavelet transformation method in order to enhance and compress the images of the data set and store the results for each process in a separate data set. Secondly, applying a hybrid method from two popular face recognition methods called Principal Component Analysis and Singular Value Decomposition for extracting feature from the images. This work applied by using a dataset contains 400 images for 40 different persons called Olivetti Research Laboratory (ORL). In calculating the distances between image vectors, Manhattan measurement is used and its show a very good results in recognition rate. From this work, it can be concluded that recognition rate increments with the decrementing in the number of dataset images and increasing the threshold value. The expended time in execution decreases in a very obvious way when using the compressed dataset rather than the enhanced dataset which its images has four times the size of the images in the compressed dataset.
Transmit precoding and Bayesian detection for cognitive radio networks with limited channel state information

Cognitive radio (CR) represents a recent direction for enabling coexistence among heterogeneous networks. It can be a potential solution for the problem of scarce spectrum available for wireless communication systems. The study here investigates the underlay and interweave paradigms for the coexistence of CR network of secondary users (SUs) with a primary network of primary users (PUs). Under underlay mode, both networks communicates concurrently using the same resources. With interweave, SU is able to communicate as long as (some) PUs are not active. Usually, underlay or interweave employs multiple antennas at SU to use the spectral resources better and manage the interference towards the primary network. Performance of the CR network under either paradigm depends largely on the amount and quality of channel state information (CSI) available about the different communication links. In practical systems, often CSI at SU has uncertainty since it is deviated from the true one or is not known at all. This uncertainty should be accounted when designing the precoding schemes for SU or otherwise the interference impact on primary networks would violate the quality of service (QoS) requirements for PUs. This dissertation considers two cases regarding to the availability of CSI, the first one is when CSI is imperfect and the second is when CSI is completely not known. For the underlay mode, we investigate two manifolds. The first one addresses the problem of maximizing the throughput of a multiple-input multiple-output (MIMO) SU when CSI of the interference link to PU is completely unknown or partially known. We study the achievable rates for SU under two different QoS requirements for the PU: the conventional interference temperature and leakage rate metrics. When CSI
Research focuses and findings of flipping mathematics classes: a review of journal publications based on the technology-enhanced learning model

ABSTRACT The present study reviewed the papers in the Scopus database relevant to flipped classrooms in all mathematics subject areas published up until 2018 from various dimensions. It was found that there has been a considerable number of studies on flipped classrooms in mathematics education in these years. The majority were conducted in university courses, such as applied mathematics, discrete mathematics/algebra, and analysis. Most of the researchers adopted quantitative methods. In the before-class stage of the mathematics flipped classroom, a number of studies employed online learning systems or social media to provide instructional videos and to carry out online discussion. As for the in-class stage, the most frequently adopted learning strategies were issue discussion, exercise, and problem-based learning, while only a few studies used technologies in this stage. The majority of studies investigated students’ learning achievement, motivation, satisfaction, and collaboration and communication awareness. Most studies administered examinations to evaluate the students’ learning achievement. It is worth noting that little or no research was found to explore students’ cognitive load. Accordingly, several suggestions for applying flipped classrooms to mathematics education as well as potential issues for future research are proposed in this study. GRAPHICAL ABSTRACT