Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Ontology Completeness via SPARQL and Relations-between-Classes Based Constraints

This article first distinguishes constraints from rules, and descriptive constraints from prescriptive ones. Both kinds can be used to calculate a constraint-based completeness (as opposed to a real-world-based completeness), i.e. evaluating how much of a knowledge base is complete with respect to some constraints, e.g. for evaluating how well this base follows given ontology design patterns or best practices. Such evaluations may also guide knowledge elicitation and modelisation. This article explores the ways constraints can be represented via relations between classes, hence via any knowledge representation language (KRL) that has an expressiveness at least equal to RDF or RDFS. Compared to the popular practice of both representing and checking constraints via queries, this approach is as simple, offers more possibilities for exploiting both knowledge and constraints, and permits the selection and use of inference engines adapted to the expressiveness of the exploited knowledge instead of the use of restricted or ad hoc constraint-validation tools. This approach is also modular in the sense it separates content from usage: the represented "content focused constraints" can then be exploited via few "content independent" queries, one for each usage and kind of constraint. This approach provides more possibilities.
Successive Human Tracking and Posture Estimation with Multiple Omnidirectional Cameras

We propose a successive method for human tracking and posture estimation by using multiple omnidirectional cameras appropriate for Machine Learning method. A stable estimation for foot and head position is executed by the combination analysis with particle filter processing. Moreover, a classification method is accomplished by using the constraint of the connected line between head and foot position. The combination both this constraint and relative height from head to foot is possible to distinguish typical four postures for human activities in an indoor scene. We believe that this continuity of each data helps smooth convergence to the time-sequential learning for the discrimination between normal and abnormal behavior.
A photogrammetric system for dendrometric feature estimation of individual trees

Dendrometry refers to the study of the geometrical dimensions of a tree as an individual entity. This paper presents a dendrometric feature estimation pipeline to calculate the tree height, diameter at breast height (DBH) and the height to the base of the crown (trunk height), taking advantage of the abilities of photogrammetric systems that produce spatially accurate 3D models using a sets of overlapping digital images and in this way, provide a technological dendrometric measurement technique. Tests were carried out on 5 trees. The corresponding 3D models were obtained, then each model was rescaled to a real measure, the data set belonging to the trunk and the crown of the tree were identified and finally, the total tree height, the height at the base of the crown and the diameter at breast height were estimated.
Graph Embedding Based Query Construction Over Knowledge Graphs

Graph-structured queries provide an efficient way to retrieve the desired data from large-scale knowledge graphs. However, it is difficult for non-expert users to write such queries, and users prefer expressing their query intention through natural language questions. Therefore, automatically constructing graph-structured queries of given natural language questions has received wide attention in recent years. Most existing methods rely on natural language processing techniques to perform the query construction process, which is complicated and time-consuming. In this paper, we focus on the query construction process and propose a novel framework which stands on recent advances in knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging the generalized local knowledge graphs. Then, given a natural language question, our framework computes the structure of the target query and determines the vertices/edges which form the target query based on the learned embedding vectors. Finally, the target graph-structured query is constructed according to the query structure and determined vertices/edges. Extensive experiments were conducted on the benchmark dataset. The results demonstrate that our framework outperforms several state-of-the-art baseline models regarding effectiveness and efficiency.
A Study of Current -Fed ZVS Boost DC/DC Converter using Quasi-Resonant Conversion

This study is to improve the performance of boost-mode conversion in bidirectional DC/DC converter (bi-dc/dc, so-called dual active bridge converter (DAB). A current-fed quasi-resonance establishes at the low-voltage side (LVS) of the bi-dc/dc converter and performs by simply adding a capacitor across the LVS DAB. A quasi-sinusoidal energy transfer from LVS to the high-voltage side (HVS) realizes by using a constant-off time control. This approach ensures the switch pair at LVS able to commutate at zero-voltage-switching (ZVS). Modeling and analysis of the current-fed boost conversion conduct in detail. We realize a 1-kW current-fed bi-dc/dc converter in boost-mode conversion to assess the predicted boost-mode performance. The power efficiency as high as about 92% achieves in whole load range.
State Recognition and Reinforcement Learning for Two-Wheel Mobile Robot

The autonomous robots recognize surrounding environments using sensors, make judgments based on algorithms, and control actuators such as motors. This paper presents a strategy to deal with unpredictable environmental changes of line following two-wheel robots. We define the running conditions of the robot from the light intensity value obtained from the sensor. Then, appropriate change amounts of the running parameters are derived by means of policy gradient reinforcement learning. With this function, the robot was able to properly provide the ability to dynamically respond to unexpected environmental changes.
Design of Perforated Membrane for Low-Noise Capacitive MEMS Accelerometers

A method is presented for designing a thick membrane with several thousand perforations suitable for low-noise capacitive micro-electro-mechanical systems (MEMS) accelerometers. Although a membrane with two-step perforations shows good noise density performance, it is difficult to simulate mechanical noise using the conventional method because there are too many perforations, which makes the structure of the membrane complicated. The proposed method is based on the assumption that the number of perforations is infinite and that there are symmetric planes between the perforations. This assumption greatly reduces computational complexity because the simulation model is simplified in the region around a perforation. We fabricated MEMS resonators with a two-step perforated membrane and evaluated the accuracy of the simulation results. The measured Q values of the fabricated resonators were in good agreement with the calculated results. Using this design method, we optimized the structure of a membrane with two-step perforations and fabricated a capacitive MEMS accelerometer. It showed excellent low-noise density (less than 15 ng/Hz1/2).
Highly Sensitive Juntionless Nanowire Transistor Biosensor in Detecting Breast Tumor Marker

Here, we demonstrate a real-time, ultrasensitive junctionless nanowire transistor (JNT) biosensor for breast tumor marker sensing. The proposed JNT biosensor with low-frequency noise, small subthreshold slope and high channel mobility has great potential to solve the key problem of poor signal-to-noise ratio or low sensitivity in current nanobiosensor. The JNT with CMOS-compatibility is made using a (111) silicon-on-insulator (SOi) wafer with a low-cost and top-down fabrication approach. The fabricated device shows excellent electrical properties and high sensitivity for recognizing breast tumor marker (the CEA protein) as low as 1fg/ml.
Sequence Impedance Modeling and Stability Analysis for Renewable Energy Power Station

The accessibility of renewable energy power station to weak energy grids is prone to harmonic disturbances and other small disturbance stability problems. Considering the influence of phase locked loop (PLL) and distributed transformer impedance, a harmonic linearization impedance modeling method is adopted to establish a system sequence impedance model for renewable energy power station in the frequency domain; equivalent principle is used to simplify the impedance model of the system to the simplest one-port network; based on the criterion of impedance stability, analyze the stability of the system by considering and investigating the dynamic interaction between the renewable energy power station and the grid; verify the number of parallel inverters and the impact of the bandwidth of the PLL on the stability of the system. The investigation shows that as the bandwidth of the PLL and the number of parallel connected inverters increase, the system stability will gradually deteriorate. The simulation results are also presented to validate the model and impedance stability analysis method.
Cost-Time Performance of Scaling Applications on the Cloud

Recent advancements in big data processing and machine learning, among others, increase the resource demand for running applications with larger problem sizes. Elastic cloud computing resources with pay-per-use pricing offers new opportunities where large application execution is constrained only by the cost budget. Given a cost budget and a time deadline, this paper introduces a measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution. We evaluate our approach with a set of representative applications that exhibit a range of resource demand growth patterns on Amazon AWS cloud. We show the existence of cost-time-size Pareto-frontier with multiple sweet spots meeting user constraints. To characterize the cost-performance of cloud resources, we use Performance Cost Ratio (PCR) metric. We extend Gustafson's fixed-time scaling in the context of cloud, and, investigate fixed-cost-time scaling of applications and show that using resources with higher PCR yields better cost-time performance. We discuss a number of useful insights on the trade-off between the execution time and the largest Pareto-optimal problem size, and, show that time deadline could be tightened for a proportionately much smaller reduction of problem size.
A Ten-Long Non-Coding RNA Model Improves Prognosis Prediction of Hepatocellular Carcinoma Patients

In this paper, the lncRNA expression profiles of 424 hepatocellular carcinoma (HCC) patients, including 374 tumor and 50 adjacent non-tumor tissues, from The Cancer Genome Atlas (TCGA) were analyzed. By univariate and multivariate Cox survival analysis, we found and validated 10 significant lncRNAs associated with HCC prognosis. Based on the risk calculation model established on these 10 lncRNAs, HCC patients samples could be divided into high-risk and low-risk groups, and the survival rates of patients in these two groups are significantly different. Our research provides data to further understand the molecular mechanisms implicated in HCC and to improve diagnosis, treatment and prognosis used for HCC patients.
Wordpress Honeypot Module

Web security issues are and will remain one of the biggest threats on the Internet in nowadays. In order to prevent some of these vulnerabilities, you can use web honeypot programs that collect data about attacks. Having information about those attacks tendencies, an important amount of the vulnerabilities of web applications in an organization can be prevented using web honeypots. In this paper we investigate design considerations for a Wordpress honeypot module, which are capable to preserve the high interaction capability of the honeypot. The experimental results show that the proposed honeypot module helps to find new vulnerabilities in plugins and themes if an attacker tries to exploit one of these.
Robust Predictive Current Control for Permanent Magnet Linear Synchronous Motor Servo System

This paper presents robust predictive current control (RPCC) to achieve high current-loop performance for permanent magnet linear synchronous motor (PMLSM) drives. Firstly, a deadbeat current predictive algorithm is proposed and the predictive control model of PMLSM is derived. Secondly, the stability of the algorithm is analyzed and the stable condition of the algorithm is obtained. A robust control method is adopted to make the control algorithm stable in the range of motor parameter mismatch. Finally, physical experiments are carried out on the TMS320F28335 DSP experimental platform. Experimental results show the proposed algorithm has better dynamic performance and robustness. The correctness and effectiveness of the proposed method are verified by experimental results.
Multi-channel Distributed MAC protocol for WSN-based wildlife monitoring

Several wild animal species are endangered by poaching. As a solution, deploying wireless sensors on animals able to send regular messages and also alert messages has been envisaged recently by several authorities and foundations. In that context, this paper proposes WildMAC, a multichannel, multihop wireless communication protocol for these specific wireless sensor networks that have to collect data from unknown large areas with different QoS requirements. WildMAC is a TDMA based MAC protocol that leverages long range communication properties to propose an efficient data collection mean. Its performance evaluation shows it meets QoS requirements.
Reducing Spurious Handovers in Dense LTE Networks based on Signal Strength Look-ahead

Handover, the process of transferring a call or data session from one base station to another without disconnection, is an important problem in LTE networks. Several handover algorithms have been proposed for LTE networks in general. However, they mostly use the current signal strengths for making handover decisions, which can cause spurious handovers in a dense eNB deployment. In this paper, we investigate the use of look-ahead signal strength information for reducing spurious handovers. We first propose a novel graph-based framework that uses signal strength information along the mobile trajectory of the UE to make better handover decisions. Two algorithms are then presented based on this framework. The first algorithm assumes that exact signal measurements at the UE from all eNBs in its trajectory are available a priori for all time instances, and provides a baseline reference for finding the minimum number of handovers that can be achieved. The second algorithm uses the exact signal measurement for the current time instance only, and estimates the signal strengths for future time instances. The performances of the algorithms are compared with four existing LTE handover algorithms using simulation on real world data. It is shown that the proposed algorithms significantly reduce the number of handovers while still maintaining good signal quality for communication throughout the trajectory of the UE.
Towards Dynamic Multi-task Schedulling of OpenCL Programs on Emerging CPU-GPU-FPGA Heterogeneous Platforms: A Fuzzy Logic Approach

Heterogeneous systems featuring multiple kinds of processors are becoming increasingly attractive due to their high performance and energy saving over the homogeneous systems. With the OpenCL as a unified programming language providing programs portability, and the recent advances in transistor technology allowing multi-core CPUs, GPUs and FPGA to be on the same chip, finding the best task-to-device mapping will be the key to gain such high performance and leverage their use from application dedicated devices to platforms for concurrent user applications. This work proposes an energy-efficient scheduling scheme to schedule concurrent OpenCl tasks targeting CPU+GPU+FPGA heterogeneous systems by setting the best kernel-device pair at run-time. The scheme is expected to provide the best mapping in terms of throughput and energy consumption under the constraints of hardware resources, concurrent execution and contention scenarios.
Three-dimensional resource allocation in space division multiplexing elastic optical networks

In this paper, we present a comprehensive model to address three-dimensional resource assignment (3D-RA) in space division multiplexing elastic optical networks using few-mode multi-core fibers (FM-MCFs). Accordingly, we present new 3D-RA algorithms in which the consequential resources include the spectrum, modes, and cores. We consider all spectral and spatial diversity types in FM-MCFs and introduce five 3D-RA scenarios including (i) single-mode and single-core (SMSC), (ii) single-mode and multi-core (SMMC), (iii) multi-mode and single-core (MMSC), (iv) multi-mode and multi-core (MMMC), and finally, (v) hybrid 3D-RA. In each scenario, the fractional joint (FrJ-) and independent (Ind-) switching (Sw) schemes are introduced and explored for the proposed scenarios. By using the FrJ- and Ind-Sw schemes, we performed the simulation process for the above-mentioned scenarios. The simulation results confirm the efficiency of the proposed 3D-RA algorithms. Furthermore, various 3D-RA scenarios are comprehensively compared using the obtained results in terms of vital metrics introduced in this work. It is also indicated that the SMSC and MMSC scenarios present superior performance for Ind-Sw and FrJ-Sw 3D-RA scenarios, respectively, in comparison to other proposed non-hybrid scenarios. The obtained results also reveal that the quad-hybrid 3D-RA scenario results in the lowest blocking probability in comparison with all other introduced scenarios in both switching schemes. These results recommend an efficient simulation system for compatible transceivers for Ind- and FrJ-Sw schemes.
A Unified Nonsingular Rapid Transfer Alignment Solution for Tactical Weapon Based on Matrix Kalman Filter

The performance of tactical weapons inertial navigation system (INS) greatly depends on the accuracy and rapidity of transfer alignment (TA). The major challenge is the estimation of the attitude of INS rapidly and accurately, under the condition of random initial misalignment angles and launch time constraint in tactical scenarios. The objective of this paper is to derive a unified nonsingular alignment scheme for rapid TA at random initial misalignment angles, which transforms the alignment problem into the estimation of the relative attitude matrix between the master INS and slave INS. Employing the attitude matrix as estimate state, we formulate a unified nonsingular linear dynamics model allowing us to estimate the attitude matrix directly. Furthermore, the attitude matrix estimation algorithm has been extended to work in a state matrix Kalman filter (MKF) framework, which we call MKFTA algorithm. It works without any assumption on the misalignment angles. Finally, Monte Carlo simulations demonstrate that the MKFTA can converge within 10 s and exhibits a better transient and steady-state accuracy than the traditional scheme.
Symbolic Execution with Invariant Inlay: Evaluating the Potential

Dynamic symbolic execution (DSE) is a non-standard execution mechanism which, loosely, executes a program symbolically and, simultaneously, on concrete input. DSE is attractive because of several uses in software engineering, including the generation of test data suites with large coverage relative to test suite size. However, DSE struggles in the face of execution path explosion, and is often unable to cover certain kinds of difficult-to-reach program points. Invariant inlay is a technique that aims to improve a DSE tool by interspersing code with invariants, generated automatically using off-the-shelf tools for static program analysis using abstract interpretation. To capitalise fully on a static analyzer, invariant inlay applies certain instrumentations and testability transformations to the program source. In this paper we outline the invariant inlay approach, and how we have evaluated the idea, in order to determine its usefulness for programs with complex control flow.
Audit to the Data Quality Management Process in a Small Organization Based on NTC-ISO 19011

Nowadays, large, medium and small companies execute audits to verify the efficiency and effectiveness of processes and products. To manage them, there are guides, methodologies, techniques that are applied in the organization with the aim of avoiding the propagation of threats and vulnerabilities. This work was carried out in the professional services area of a small company, in which the process of Data Quality Management was taken and the audit was conducted to verify the current status of said process and identify recommendations and improvements in its execution, in order to contribute to the improvement of the quality of the developed projects. The audit was conducted based on the NTC-ISO 19011 standard, which was selected with the analyzes and results obtained in the comparison made between different existing frameworks, based on the literature and the evaluation carried out through the Delphi methodology.
Short text sentiment analysis based on convolutional neural network

In recent years, with the development of social media, a large amount of text has appeared on the Internet. Weibo, as the most popular micro blog service in China provides abundant information about netizens' attitudes. The application of sentiment analysis on Weibo's massive data will help improve the Internet's public opinion monitoring system to detect abnormal or unexpected events in the physical world. In this paper, we will use Convolutional Neural Network to make an effective analysis of user comments based on various text collected from Weibo. The experimental results show that compared with the traditional method, our model has achieved significant and consistent improvement.
Prediction of Manufacturing Processes Errors: Gradient Boosted Trees Versus Deep Neural Networks

In this paper we investigate the use of machine learning techniques for optimizing manufacturing processes operation. More precisely we propose, compare and contrast two approaches for predicting errors in manufacturing processes. The first approach is based on machine learning algorithms while the second one uses deep learning techniques. Both approaches are validated using a dataset from literature, the SECOM dataset, which is representative for manufacturing processes. For the machine learning approach features are selected using the Multivariate Adaptive Regression Splines (MARS) algorithm and data is classified using the Gradient Boosted Trees (GBT) algorithm, while for the deep learning approach features are selected using a Support Vector Machine (SVM) algorithm and data is predicted using a Neural Network (NN). The evaluation results show that the best results are obtained using the deep learning approach.