Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Convolutional Extreme Learning Machine

Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don’t apply to non-Euclidean data, e.g., graph-structured data. To overcome this shortcoming, this paper presents a Graph Convolutional Extreme Learning Machine (termed as GCELM) for semi-supervised classification. Technically, a random graph convolutional layer is introduced to replace the random projection of original ELM, which endues ELM with the capability of dealing with graph-structured data directly. To generate a robust graph from the raw dataset, a self-representation model is adopted to construct a weighted graph. Extensive experiments on 27 UCI datasets demonstrate that GCELM outperforms many popular semi-supervised methods, and with faster learning speed. To the best of our knowledge, this is the first work that combines graph convolution with ELM.
Overview of Vehicle Optical Wireless Communications

Effective information transmission between vehicles has become one of the solutions to improve road safety. Based on the huge advantages of optical wireless data transmission technology, this article summarizes the research status of unmanned vehicles in optical wireless communication (OWC). The article not only studies the actual progress of building optical links through an unmanned vehicle, connecting air communication, ground communication, underwater communication into a unified wireless network, and realizing the fast, stable, large-capacity data transmission system. The technical challenges of unmanned vehicle OWC are also discussed and its future development has prospected.
Dynamic Gestures Recognition through a Low-cost Data Glove

This paper presents some results towards the realization of a commercial wearable device for sign language translation. In particular, we propose an entire workflow for recognizing dynamic gestures. Different techniques are analysed and compared, both to realize a database of gestures’ features and to classify a performed gesture. We test the proposed method on dataset of 10 dynamic gestures, acquiring data of two IMUs embedded in a data-glove. Different classifiers achieve a 100% of accuracy, using spline fitting as feature extraction technique.
Toward Mobile 3D Vision

In the past few years, the computer vision community has developed numerous novel technologies of 3D vision (e.g., 3D object detection and classification and 3D scene segmentation). In this work, we explore the opportunities brought by these innovations for enabling real-time 3D vision on mobile devices. Mobile 3D vision finds various use cases for emerging applications such as autonomous driving, drone navigation, and augmented reality (AR). The key differences between 3D vision and 2D vision mainly stem from the input data format (i.e., point clouds or 3D meshes vs. 2D images). Hence, the key challenge of 3D vision is that it is could be more computation intensive and memory hungry than 2D vision, due to the additional dimension of input data. For example, our preliminary measurement study of several state-of-the-art machine learning models for 3D vision shows that none of them can execute faster than one frame per second on smartphones. Motivated by these challenges, we present in this position paper a research agenda on offering systems support for real-time mobile 3D vision, focusing on improving its computation efficiency and memory utilization.
Enhancing Shoulder Pre-Movements Recognition Through EEG Riemannian Covariance Matrices For a BCI-based Exoskeleton

This work proposes a system to recognize motor intention during shoulder flexion/extension, in order to convey control commands towards an upper-limb robotic exoskeleton. For this purpose, two recognition systems were explored: 1) spatial features from Riemannian geometry and linear classification; and 2) common average reference pre-processing, feature extraction from time and frequency domains, and support vector machine classification. The effects of varying window sizes were also explored to anticipate shoulder flexion/extension, along with varying frequency bands, electroencephalography (EEG) channel arrangements, and classifier types. For some participants, our proposed system achieved Kappa values higher than 0.74 during shoulder movement recognition. Moreover, average accuracy (ACC) $\geq 67$%, Kappa $\geq 0.34$, and false positive rate (FPR) $\leq 33$% during shoulder motor anticipation were obtained, thus suggesting the potential usefulness of the proposed method for robotic exoskeleton control.
Multi-Band and High Gain Antenna Using AMC Ground Characterized With Four Zero-Phases of Reflection Coefficient

In this paper, a multi-band AMC-backed antenna system characterized with high gain and low-profile is proposed. It is constructed by placing a quad-band coplanar-waveguide (CPW) antenna over an AMC with four zero-phases in the reflection coefficient. The CPW antenna consists of an ornamental-pillar-shaped patch with the specially designed parasitic elements, and a defected ground structure (DGS), for the application in the frequency bands of 2.45, 3.5, 4.6 and 5.8 GHz. To improve the CPW antenna’s radiation performance, an artificial magnetic conductor (AMC) ground with four zero-phases of the reflection coefficient is designed and employed as the reflector. The AMC unit cell is composed of four metallic nested rings (FMNR), whose innermost ring is connected in series to four lumped capacitors. It is observed that the proposed quad-band AMC ground can not only increase the gains of the multi-band antenna greatly but also combine the two intermediate frequency bands of the antenna into a broadband one. The prototypes of the antenna and the AMC array with $5\times {5}$ unit cells were fabricated and measured. It is found that in comparison to the antenna without AMC, the AMC-backed antenna obtains a gain enhancement by amounts of 4.93, 5.92, 5.54 and 4.95 dB at the frequencies of 2.45, 3.5, 4.6 and 5.8 GHz. The 10-dB impedance bandwidths of the AMC-backed antenna include three bands of 2.13–2.87, 3.22–4.75, and 5.54–5.86 GHz, with the corresponding relative bandwidths of 14.8%, 38.4%, and 5.3%. The proposed antenna can be potentially applied to the applications in WLAN, WiMAX, and 5G mobile communication systems.
Vibration Control of Truck Cabins With the Adaptive Vectorial Backstepping Design of Electromagnetic Active Suspension System

The active suspension system is an important equipment that isolates the vibrations that may come from outside in a land vehicle. In heavy vehicles, the active suspension system can be used to dampen vibrations in the driver’s cabin. The electromagnetic actuator is used as an active element in suspension system. Traditionally, the use of active suspension systems is mainly on automobiles and the studies related to heavy vehicles like trucks lack enough interest. In this study, dynamic modeling of a three-axle heavy vehicle cabin is performed with a half-car approach and it is aimed to suppress the disruptive effects coming from the road with active electromagnetic actuators. Lyapunov based backstepping control design is expressed in vectorial form for the heavy vehicle system, which is a multi-input multi-output system. To demonstrate the performance of the designed controller, a comparison has been made for the active and passive states for the truck cabin suspension system. The main feature of the applied control method is that it does not require knowing the actuator parameters, the adaptive term can handle the estimate of the actuator parameter. The stability of the proposed system has been proven via Lyapunov based arguments and given simulation works. The results obtained are important to suppress vibration, consequently, decreases the vibration exposure for truck drivers in terms of occupational health and safety aspects.
Dynamic Economic Load Dispatch in Isolated Microgrids with Particle Swarm Optimisation considering Demand Response

A viable option for electrification of remote areas far from power grids is to set up microgrids and feed them with local generation. Such microgrids are referred to as isolated microgrids and due to the lack of possibility of power exchange with the grid, their operation is different from grid-connected microgrids. Isolated microgrids, similar to grid-connected microgrids are equipped with energy management systems including unit commitment and economic dispatch modules. In this paper, the aim is to formulate the dynamic economic load dispatch (DELD) in isolated microgrids, while curtailment of responsive loads and curtailment of renewable power is allowed and load shedding is used as the last resort for balancing generation and demand. The generated power of dispatchable distributed generators (DGs), curtailed power of renewable DGs, curtailed demand and shed power are determined for each time period. The formulated DELD problem is solved with the well-established particle swarm optimisation (PSO) algorithm. The results for a microgrid with four dispatchable DGs and two renewable DGs show the performance of PSO over grey wolf optimisation (GWO) and also indicate the significant effect of demand response in reducing the operation cost of isolated microgrids.
IFVD: Design of Intelligent Fusion Framework for Vulnerability Data Based on Text Measures

Security vulnerability database research is an essential part of information security research. With the sharp increase in the number of vulnerabilities in recent years, it has become increasingly important to collect and organize information about existing vulnerability databases. However, there is heterogeneity and redundancy of data between the databases, which makes it challenging to share vulnerability information. In response to the above problems, a comprehensive security vulnerability collection model is proposed. A total of 1.005 million pieces of vulnerability data are collected and analyzed in 11 mainstream vulnerability databases. By introducing the idea of the collection of automation, problems such as the untimely update of vulnerability database information and the low efficiency of vulnerability collection, are solved effectively. The structural framework and functional modules of the automatic vulnerability collection system are introduced, and the specific implementation of the system is given. Based on the text processing technology, the rules of deduplication (95.6% accuracy) and the intelligent framework for vulnerability database (IFVD) are proposed and implemented. Finally, Experiments show the feasibility of the scheme.
ToA Positioning Algorithm for TDoA System Architecture

This paper is devoted to synthesis of algorithm of determining coordinates for navigation systems with TDoA architecture and testing it positioning accuracy comparing with standardly used TDoA algorithm. The proposed approach can be used in ultrasonic systems, passive radar systems, marine echolocation, LTE signal positioning, as well as in many other systems where TDoA architecture is implemented. The gain of proposed approach in positioning accuracy amounted to almost 30% in conducted simulation with different types of object movement on the example of UWB LPS. The paper is divided into 5 parts: Introduction, Types of UWB LPS infrastructure, ToA algorithm, modelling and analysis, and conclusion. In first section (Introduction)we aim to provide a comprehensive survey of different indoor localization techniques and technologies. In second section (Types of UWB LPS architecture) basic ranging methods and architectures in navigation radio system, as well as main factors affecting the positioning accuracy are presented. In third section (ToA algorithm) we describe in detail proposed approach of ToA positioning for TDoA-based systems. In fourth section (Modelling and analysis) obtained results of conducted simulation modelling, which tests proposed ToA algorithm, are shown. In conclusion, we summarize the studies described in paper, present the gain value obtained in modelling results and provide our further steps in work in this area.
Investigating Different Levels of Mobile Banking Usage: A Mixed-Methods Study in China

M-banking usage has attracted great attention from literature in recent years. The goal of this study is to provide new insights into m-banking usage by investigating the motivations for m-banking usage and how these motivations can drive different levels of m-banking usage respectively. A quantitative dominant mixed-methods design is applied in this study. First, a qualitative study through interviews is conducted to identify the motivations for m-banking usage. Five m-banking motivations are identified from the qualitative study, namely convenience, cost saving, information gaining, word-of-mouth and decision-making quality. Second, a quantitative study through survey is conducted to test the effects of motivations on different levels of m-banking usage. In general, results indicate that different levels of m-banking usage could be driven by different m-banking motivations.
The Predicting Collision with External Obstacles of Dual-Arm Multi-Link Robot

The article proposes a method for solving the external obstacles predicting problem of dual-arm multi-link robot. The method is based on the linear algebra approach by considering the Schwartz inequality. The proposed approach will allow the prediction of possible collision of the external obstacles and robot’s manipulators. The application of the proposed approach is possible when implementing robot control based on the «Programming by demonstration» approach in case of wrong actions of the operator («teacher»), leading to a self-collision of manipulators. An example of using the presented approach is considered. The developed algorithm was tested when constructing the control of an anthropomorphic robot SAR-401 and of the simulator of underwater dual-arm robot with anthropomorphic manipulators.
Prediction Vehicle’s Speed with Using Artificial Neural Networks

We propose an approach for the vehicle’s speed prediction based on artificial neural networks. Different types of artificial neural networks were considered including MLP and RNN. A complex urban route in Kazan city was chosen for data gathering and making experiments. We demonstrated that it is possible to obtain sufficient accuracy for speed prediction based on limited source data. We got the prediction accuracy as 99.6% for the first future second and 94% for the tenth future second. Simple RNN showed better results for given data. We can use the suggested approach for designing intellectual automatic transmission systems and other intelligent transport systems applications.
Communication Structure Design of Controllable Residential Loads

A large number of controllable loads on the user side, especially scattered and flexible controllable residential loads, plays an important role in the safe, reliable and economic operation of the power grid. At present, they have been widely used in many aspects of the power grid such as demand-side management, dynamic security enhancement, etc. In order to achieve the integration of multiple controllable loads in the power grid, not only a reliable control strategy is required, but also need a communication structure with good performance including strong load-carrying capacity, low latency and appropriate network throughput. This communication structure would support the transmission of data packets or control commands between the controllable loads and the power grid. This paper proposes a three-tier communication structure of controllable residential loads, establishes a corresponding model based on OPNET and simulates in four different scenarios by collecting three statistics to verify the feasibility of this model.
Bi-Normal Mesh Smoothing Based on Vertex Feature

Aiming at the issue that mesh smoothing is hard to balance in terms of noise removing and feature preserving, in this article, we combine the facet normal representing global geometric features of the mesh with the vertex normal characterizing local details of the mesh and propose a bi-normal mesh smoothing method based on vertex feature selection (BNBVF). Firstly, the guided filtering algorithm is extended to calculate accurately the facet normal in the field of geometric processing. The key of this portion is computing the guided normal of facet by using a geometric neighboring patch with the most consistent normal. Then, an adaptive tensor voting method is employed to divide the vertices of the mesh into feature vertices and non-feature vertices. Thirdly, a method of the neighborhood facets clustering combining with the plane fitting is proposed to calculate the normal of feature vertex, and the weighting average of first-order neighborhood facets of the vertex is applied to compute the normal of non-feature vertex. Finally, the vertices of the mesh are updated iteratively by combining the geometric information of the facet normal and vertex normal to achieve mesh smoothing. Experimental results demonstrate that the superior performance of our proposed algorithm to state-of-the-art approaches in feature preserving and error reducing.
Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

This study proposes a novel operational planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process, and hot water is stored in an attached tank. Generating and storing hot water based on an optimal operational plan according to household demand leads to further energy saving; therefore, operational planning methods based on household demand prediction have received significant attention. However, the improvement in the demand prediction accuracy does not necessarily lead to efficient PEFC-CGS operation in terms of operational costs; in other words, the accuracy in the demand prediction does not directly indicate the resulting operational efficiency. In this study, the authors propose a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates. In the proposed scheme, the error between expected and actual operational costs explicitly represents the relevance of the operational plan, so that the optimal operational plan can be selected directly from the perspective of the resulting operational efficiency. The practicality of the proposed approach is evaluated with the existing demand prediction-based approach via numerical simulations using real-world measurements of multiple customers in Japan. The proposed method reveals 30% reduction of the excessive operational costs by avoiding the inefficient operation of the auxiliary gas-heater in the experiments and will further enhance the value of introducing highly efficient residential fuel cell system that contributes to a low-carbon society.
Prediction of Personal Cardiovascular Risk using Machine Learning for Smartphone Applications

Cardiovascular disease is a major global health burden. Machine learning may be used on big data from national surveys to develop models that predict various cardiovascular risk factors. We used machine learning to evaluate and compare generalized linear, stochastic gradient boosting, random forest, and neural network model performance on predicting cardiovascular risk factors, such as hypertension, body mass index, and total cholesterol level on 5,992 adults in the US National Health and Nutrition Examination Survey (NHANES). The highest accuracy of 73% was found for predicting hypertension status, using a random forest model on a combination of demographic, diet and physical activity behavior, and mental state predictor variables. We demonstrate the use of the machine learning model through the development of an Application Programming Interface (API), which is called by a mHealth smartphone app and web interface. This work has promise for future intervention studies that assess how users respond to feedback on cardiovascular risk predictions, and which could evaluate improvements in costeffective cardiovascular healthcare.
Analysis of the Most Common Software and Hardware Vulnerabilities in Microprocessor Systems

The relevance of data protection is related to the intensive informatization of various aspects of society and the need to prevent unauthorized access to them. World spending on ensuring information security (IS) for the current state: expenses in the field of IS today amount to $81.7 billion. Expenditure forecast by 2020: about $105 billion [1]. Information protection of military facilities is the most critical in the public sector, in the non-state - financial organizations is one of the leaders in spending on information protection. An example of the importance of IS research is the Trojan encoder WannaCry, which infected hundreds of thousands of computers around the world, attacks are recorded in more than 116 countries. The attack of the encoder of WannaCry (Wana Decryptor) happens through a vulnerability in service Server Message Block (protocol of network access to file systems) of Windows OS. Then, a rootkit (a set of malware) was installed on the infected system, using which the attackers launched an encryption program. Then each vulnerable computer could become infected with another infected device within one local network. Due to these attacks, about $70,000 was lost (according to data from 18.05.2017) [2]. It is assumed in the presented work, that the software level of information protection is fundamentally insufficient to ensure the stable functioning of critical objects. This is due to the possible hardware implementation of undocumented instructions, discussed later. The complexity of computing systems and the degree of integration of their components are constantly growing. Therefore, monitoring the operation of the computer hardware is necessary to achieve the maximum degree of protection, in particular, data processing methods.
A Hybrid, Wearable Exoskeleton Glove Equipped With Variable Stiffness Joints, Abduction Capabilities, and a Telescopic Thumb

Robotic hand exoskeletons have become a popular and efficient technological solution for assisting people that suffer from neurological conditions and for enhancing the capabilities of healthy individuals. This class of devices ranges from rigid and complex structures to soft, lightweight, wearable gloves. In this work, we propose a hybrid (tendon-driven and pneumatic), lightweight, affordable, easy-to-operate exoskeleton glove equipped with variable stiffness, laminar jamming structures, abduction/adduction capabilities, and a pneumatic telescopic extra thumb that increases grasp stability. The efficiency of the proposed device is experimentally validated through five different types of experiments: i) abduction/adduction tests, ii) force exertion experiments that capture the forces that can be exerted by the proposed device under different conditions, iii) bending profile experiments that evaluate the effect of the laminar jamming structures on the way the fingers bend, iv) grasp quality assessment experiments that focus on the effect of the inflatable thumb on enhancing grasp stability, and v) grasping experiments involving everyday objects and seven subjects. The hybrid assistive, exoskeleton glove considerably improves the grasping capabilities of the user, being able to exert the forces required to execute a plethora of activities of daily living. All files that allow the replication of the device are distributed in an open-source manner.