Miracle-dz/UniFAR-FTUs huggingface dataset:
Robot vision ultra-wideband wireless sensor in non-cooperative industrial environments

In this article, the ultra-wideband technology for localization and tracking of the robot gripper (behind the obstacles) in industrial environments is presented. We explore the possibilities of ultra-wideband radar sensor network employing the centralized data fusion method that can significantly improve tracking capabilities in a complex environment. In this article, we present ultra-wideband radar sensor network hardware demonstrator that uses a new wireless ultra-wideband sensor with an embedded controller to detect and track online or off-line movement of the robot gripper. This sensor uses M-sequence ultra-wideband radars front-end and low-cost powerful processors on a system on chip with the advanced RISC machines (ARM) architecture as a main signal processing block. The ARM-based single board computer ODROID-XU4 platform used in our ultra-wideband sensor can provide processing power for the preprocessing of received raw radar signals, algorithms for detection and estimation of target’s coordinates, and finally, compression of data sent to the data fusion center. Data streams of compressed target coordinates are sent from each sensor node to the data fusion center in the central node using standard the wireless local area network (WLAN) interface that is the feature of the ODROID-XU4 platform. The article contains experimental results from measurements where sensors and antennas are located behind the wall or opaque material. Experimental testing confirmed capability of real-time performance of developed ultra-wideband radar sensor network hardware and acceptable precision of software. The introduced modular architecture of ultra-wideband radar sensor network can be used for fast development and testing of new real-time localization and tracking applications in industrial environments.
Use of Markov Decision Process Models in Preventive Medicine

The biggest trade-off when proposing health care policies is about balancing the effectiveness and the practicality of the policies. The optimal policies providing benchmark performances can be driven through using operations research tools; however, they usually have complex structures that are necessary to sufficiently represent various aspects of the system being modeled. There are also policies either proposed in guidelines or followed in practice but they often vary with the system characteristics, i.e., preferences of the clinicians, available resources of the clinics, etc. Therefore, standardized, simple yet effective policies are needed for many healthcare applications, including preventive medicine. At this point, we study developing health care delivery policies that maximize the effect of the preventive interventions, while providing applicable policy structures that can be easily followed by health practitioners in practice. We focus on two applications of preventive medicine: childhood vaccine administration practices in developing countries; and colorectal cancer screening and surveillance. Vaccine administration practices in developing countries suffer from open-vial wastage. Doses remaining from opened vials are disposed at the end of a day, due to lack of appropriate cold storage conditions. We propose administering vaccines from different sizes of multi-dose vials to address the open-vial wastage problem. We utilize a Markov decision process model to maximize the expected total number of doses administered via reducing vaccine wastage. The model dynamically decides which size of a multi-dose vial to open next, and when to terminate vaccination service for the day, given the time remaining in the replenishment cycle and available vaccine stocks. We show that the optimal policies are of control-limit type. Using data for routine pediatric vaccines, we show that the proposed optimal policies could cost-effectively reduce open-vial wastage and significantly improve the covered vaccine demand. We also analyze the initial vaccine inventory composition that specifies how many vials of each size should be kept in stock. We show that the optimal policy for the right vaccine inventory composition may improve the expected vaccine demand covered up to target levels without early termination of vaccination service while realizing reasonably small or no additional cost. Although the number of system
Monitoring sedation levels by EEG spectral analysis.

powerspectra wereobtained foragroup of37volunteers undergoing sedation with enflurane atdifferent concentrations inair. Inpart one,17subjects weregiven 0.5%, 0.75%, and1.0%for4minat eachlevel, andrecovery after 5minwasassessed bytheTrieger method. There wasconsiderable variation insubject response tothedifferent doses, butadequate sedation wasindicated by thepresence ofastrong alpha rhythm (9-11 Hz) andsuppression offrequencies below 5Hz. Overdose wasindicated byaninitial shift inthe alpha frequency toalower value (6-7Hz) followed bytheappearance ofdelta waves (0.5-4 Hz)andloss ofalpha waves. Inpart two, 20volunteers inhaled enflurane at0.5%for10 mintoallow adequate absorption, followed bya 10-min recovery period. Equal numbers showed sedation orafailure torespond toenflurane at this concentration. Intheresponders, sedation wasaccompanied byamarked shift intheratio of thepowerintwofrequency bands: 1-4Hzand 8-12Hz.Progress ofthefrequency bandpower ratio followed closely thestate ofthesubject into sedation, overdose, andrecovery. This measure wasfurther improved bytheuseofmultivariate analysis, which showed gooddiscrimination ofthe alert, sedated, andoverdosed states ofthe subject.
Design and optimization of full decoupled micro/nano-positioning stage based on mathematical calculation

Abstract. Nano-positioning is widely used in Micro-electromechanical Systems (MEMS), micromanipulator and biomedicine, coupling errors and tiny output displacements are the main disadvantages of the one. A totally uncoupled micro/nano-positioning stage with lever amplifiers is designed and tested in this paper. It is fully symmetrical along with the x- and y-directions. For obtaining large output displacements, two fully symmetric two-stage lever displacement amplifiers are utilized to amplify output displacements of piezoelectric actuators (PZTs). The established models for performances evaluation of the stage, in terms of kinetostatics, amplification ratio, reachable workspace, the input and output stiffness, are verified by finite element analysis (FEA). After that, the dimensional optimization is also carried out through the genetic optimization algorithm.The prototype of the mechanism is fabricated by using Wire-Electrical-Discharge-Machining (WEDM) process. Testing results indicate that the proposed micromanipulator demonstrates good performance.
E‐service quality as antecedent to e‐satisfaction

Purpose – The purpose of this paper is to analyse the influence of culture on the relationship between each of the dimensions of e‐service quality and satisfaction with a web site involving purchase of a tourism service.Design/methodology/approach – A questionnaire was administered to 300 international tourists (150 British and 150 Spanish) who had purchased a tourism product via the internet. The study employs a structural equation model to analyse the effect of cultural dimensions on the relationship between e‐service quality and online tourist satisfaction. The original data were standardised in order to allow comparison.Findings – The results show that the influence of the dimensions of service quality on the tourists' satisfaction with their online purchases is moderated by cultural dimensions, namely uncertainty avoidance and individualism/collectivism.Research limitations/implications – In this study only two cultures are used to analyse the moderating effect of cultural dimensions on the relations.
Intelligent Interaction with Virtual Geographical Environments Based on Geographic Knowledge Graph

The core of intelligent virtual geographical environments (VGEs) is the formal expression of geographic knowledge. Its purpose is to transform the data, information, and scenes of a virtual geographic environment into “knowledge” that can be recognized by computer, so that the computer can understand the virtual geographic environment more easily. A geographic knowledge graph (GeoKG) is a large-scale semantic web that stores geographical knowledge in a structured form. Based on a geographic knowledge base and a geospatial database, intelligent interactions with virtual geographical environments can be realized by natural language question answering, entity links, and so on. In this paper, a knowledge-enhanced Virtual geographical environments service framework is proposed. We construct a multi-level semantic parsing model and an enhanced GeoKG for structured geographic information data, such as digital maps, 3D virtual scenes, and unstructured information data. Based on the GeoKG, we propose a bilateral LSTM-CRF (long short-term memory- conditional random field) model to achieve natural language question answering for VGEs and conduct experiments on the method. The results prove that the method of intelligent interaction based on the knowledge graph can bridge the distance between people and virtual environments.
Data-Driven Predictive Probability Density Function Control of Fiber Length Stochastic Distribution Shaping in Refining Process

Pulp is the most important raw material in paper industries, whose fiber length stochastic distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the FLSD shaping, which displays non-Gaussian distributional properties. Therefore, the traditional control method based on the mean and variance of the fiber length is difficult to control the FLSD shaping effectively. In this article, a novel data-driven predictive probability density function (PDF) control method is proposed for the FLSD shaping in the refining process. First, the PDF of FLSD shaping is approximated by a radial basis function neural network (RBF-NN) and the parameters of each RBF basis function are tuned by using an iterative learning law. Second, the random vector functional link network (RVFLN)-based data-driven modeling method is employed to construct the prediction model of the weight vector. Consequently, the predictive controller is designed based on the constructed PDF model of the FLSD shaping in the refining process and the stability issue of the resulted closed-loop system is discussed. The experiments using industrial data are given to illustrate the effectiveness of the proposed method. Note to Practitioners—Pulp quality control in the refining process plays a critical role in the optimization of product quality and energy saving in the pulping and papermaking processes. Different from the conventional control method based on the mean and variance of the fiber length, a novel data-driven predictive PDF control method is proposed for the non-Gaussian stochastic distribution dynamic characteristics of the fiber length, which is used to achieve the desired PDF shaping of fiber length distribution. This kind of novel control method includes the control of the traditional mean and variance of the fiber length in some sense and has applications that are more extensive.
Wireless Sensor Network Based Health Monitoring System for Hypertensive In-Patients

Hypertensive-patient monitoring is a continuous process of observing closely the situation of patient's blood pressure and alerting the appropriate personnel in case of any anomaly. It usually requires the use of non-invasive sensors that are hardwired to bedside monitors. Although, present systems allow continuous monitoring of patient vital signs and limit the patient to the bed, the readings are mostly stored on the system local memory over a period of time before it is assessed for analysis. Hence, the need for a real time hypertensive patients’ monitoring system which can meet up with immediate demands of emergency cases. This paper presents a Wireless Sensor Network (WSN)-based health monitoring system that addressed the aforementioned drawbacks for monitoring hypertensive in-patients. The design of the system comprises of hardware components such as blood pressure sensor, Bluetooth serial communication circuit, sensor node for base station interfaces and software components. Performance evaluation of the designed system gave an accuracy of 89.7% in blood pressure monitoring. The system is also cost effective, reliable and user friendly when compared with existing systems. Keywords— Blood Pressure, Health monitoring, Hypertension, Wireless Sensor Networks
The development of verb constructions in spoken learner English

Based on datasets of L1 Italian and Spanish learner language culled from the Trinity Lancaster Corpus Sample, this paper investigates how verb-argument constructions (VACs) develop in the spoken English of L2 learners across proficiency levels. In addition to proficiency and L1 effects, we focus on the potential influence of native English usage on learner VAC production. Insights into learners’ productive knowledge of five target VACs and the verbs used in those VACs are gained through (1) comparisons of normalized entropy scores for verbs in VACs; (2) correlation analyses comparing for each VAC the verbs produced by groups of learners and by native English speakers; and (3) regression analyses comparing learner verb-VAC associations against indices of VAC usage, including verb-VAC frequency, VAC-verb association strength and contingency. Results indicate that, across L1 backgrounds, more proficient learners are more productive in their VAC use and closer to patterns in L1 English usage than less proficient learners.
<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="zou-ieq1-2942930.gif"/></alternatives></inline-formula>VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="zou-ieq2-2942930.gif"/></alternatives></inline-formula>VulDeePecker. The key insight underlying <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="zou-ieq3-2942930.gif"/></alternatives></inline-formula>VulDeePecker is the concept of <italic>code attention</italic>, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="zou-ieq4-2942930.gif"/></alternatives></inline-formula>VulDeePecker. Experimental results show that <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="zou-ieq5-2942930.gif"/></alternatives></inline-formula>VulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities.
Identification of River Hydromorphological Features Using Histograms of Oriented Gradients Cascaded to the Viola-Jones Algorithm

In this paper, a quadcopter equipped with a camera was used to capture images from a river. These captured images were used as training data in the automated detection program used to identify the hydromorphological features in the area of the river such as trees, roofs, roads and the shore. The histogram of oriented gradient with support vector machine classifier was cascaded with the Viola Jones Algorithm in order to recognize hydromorphological features. Testing was done using different images to verify the effectiveness of the detection system compared with previous studies. System evaluation and success of the cascaded system was determined using the percentage of correct detected features in the image. The results showed that the cascaded system has increased the accuracy compared to the implementation with only the Viola Jones Algorithm. 
MISSILE: A System of Mobile Inertial Sensor-Based Sensitive Indoor Location Eavesdropping

Privacy concerns on smartphones have been raised by the public as more and more personal data are now stored on them. In this paper, we show that location information can be compromised through mobile inertial sensors which are considered insensitive and accessible by any mobile application in both iOS and Android without special privilege. We present MISSILE, an automatic system that can infer users’ indoor location using labeled sensor data as prior knowledge. The key idea is that when a user reaches a particular indoor location, it is very likely that he/she has passed through some unique interior structures of a building, such as winding corridors, fire stop doors or elevators. These structures exhibit repeatable motion and environment patterns in mobile sensors that can be recognized by supervised learning. In our MISSILE system, the location labels of training data are automatically attained by Bluetooth beacons deployed in sensitive locations. With effective feature extraction procedure robust modeling, MISSILE shows good success rate for inference attack. For example, in a university campus with 15 sensitive locations, MISSILE achieves up to 73% correct prediction score whereas a random guess can only achieve 1/(15 + 1) = 6.25%. Further improvements on system performance and countermeasures are also discussed.
Radio Number for Generalized Petersen Graphs $P(n,2)$

Let <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> be a connected graph and <inline-formula> <tex-math notation="LaTeX">$d(\mu,\omega)$ </tex-math></inline-formula> be the distance between any two vertices of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>. The diameter of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> is denoted by <inline-formula> <tex-math notation="LaTeX">$diam(G)$ </tex-math></inline-formula> and is equal to <inline-formula> <tex-math notation="LaTeX">$\max \{d(\mu,\omega); \\ \mu,\omega \in G\}$ </tex-math></inline-formula>. The radio labeling (RL) for the graph <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> is an injective function <inline-formula> <tex-math notation="LaTeX">$\digamma:V(G)\rightarrow N\cup \{0\}$ </tex-math></inline-formula> such that for any pair of vertices <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\omega \,\,|\digamma (\mu)-\digamma (\omega)|\geq diam(G)-d(\mu,\omega)+1$ </tex-math></inline-formula>. The span of radio labeling is the largest number in <inline-formula> <tex-math notation="LaTeX">$\digamma (V)$ </tex-math></inline-formula>. The radio number of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>, denoted by <inline-formula> <tex-math notation="LaTeX">$rn(G)$ </tex-math></inline-formula> is the minimum span over all radio labeling of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>. In this paper, we determine radio number for the generalized Petersen graphs, <inline-formula> <tex-math notation="LaTeX">$P(n,2)$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$n=4k+2$ </tex-math></inline-formula>. Further the lower bound of radio number for <inline-formula> <tex-math notation="LaTeX">$P(n,2)$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$n=4k$ </tex-math></inline-formula> is determined.
Machine learning-based multi-modal information perception for soft robotic hands

This paper focuses on multi-modal Information Perception (IP) for Soft Robotic Hands (SRHs) using Machine Learning (ML) algorithms. A flexible Optical Fiber-based Curvature Sensor (OFCS) is fabricated, consisting of a Light-Emitting Diode (LED), photosensitive detector, and optical fiber. Bending the roughened optical fiber generates lower light intensity, which reflecting the curvature of the soft finger. Together with the curvature and pressure information, multi-modal IP is performed to improve the recognition accuracy. Recognitions of gesture, object shape, size, and weight are implemented with multiple ML approaches, including the Supervised Learning Algorithms (SLAs) of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and the unSupervised Learning Algorithm (un-SLA) of K-Means Clustering (KMC). Moreover, Optical Sensor Information (OSI), Pressure Sensor Information (PSI), and Double-Sensor Information (DSI) are adopted to compare the recognition accuracies. The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective. The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higher than 85 percent for almost all combinations. Moreover, DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.
Queuing in terms of complex systems

Limited resources are a natural feature of most real systems, both artificial and naturalones. This causes the need for effective management of access to existing resources. In this area,queuing systems are of special application. However, they are treated as simple systems for whichtwo states are characteristic: work underload and work on the border of thermodynamic equilibrium.This approach is reflected in existing queue management mechanisms, that need to keep them in oneof two mentioned states. On the other hand, they should be considered from the point of complexsystems view, for which the third operation states: overload state is natural as well. In order to becloser to this issue, in this paper the authors consider queues performance from the perspective ofcomplex systems.