Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey of ontology learning techniques and applications

Abstract Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.
Manipulation of Non-Magnetic Microbeads Using Soft Microrobotic Sperm

In this work, we demonstrate the ability of soft microrobotic sperms to manipulate non-magnetic microbeads in two-dimensional space. First, we model the interaction between the microrobotic sperm and microbeads using the resistive-force theory (RFT). This RFT-based model enables us to predict the maximum payload a soft microrobotic sperm can manipulate at different actuation frequencies. Second, we demonstrate manipulation of the microbeads using microrobotic sperm under the influence of controlled magnetic fields. Our teleoperation manipulation trials show that the microrobotic sperm swims at an average speeds of 0.16 and 0.035 body-Iength-per-second during collision-free locomotion and manipulation, respectively. In addition, the microrobotic sperm positions 2-microbead within the vicinity of a reference position with maximum steady-state error of $55\ \mu \text{m}$.
Automatic Design of Evolutionary Algorithms Based on Entropy Triggers

The field of automatic algorithm design has received increasing attention in recent years. From a multitude of available algorithms, a researcher can effectively design a new one customized to his/her own problem. For this, hyper-heuristics techniques have proven to be useful. Their main objective is to search in the space of heuristics rather than in the problem solution space. The present paper proposes a hyper-heuristic for the automatic design of evolutionary algorithms supported by the use of an entropy metric. This metric is used as a trigger mechanism for switching between the algorithms components, aiding the formation of the new hybrid algorithm.
Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data

Research on the contribution of understory components to the total above ground biomass (AGB) has to date received very little attention because most prior biomass estimation studies have ignored small regenerating trees beneath the main canopy with the assumption that their contribution to biomass is generally negligible. Only a few biomass studies have emphasized a considerable contribution to biomass of understory components in forest ecosystems. However, this study of native, tropical, deciduous forest biomass in the Central Highlands of Vietnam was able to explore the contribution of small regenerating trees to total biomass by exploiting a large field inventory of hundreds to thousands of individually-counted small regenerating trees per hectare. Thus, this study investigated the influence of small regenerating tree biomass on models of the relationship between total AGB and remote sensing data. These analyses were trained with and without topographic variables derived from ASTER-GDEM. Our results demonstrate that the inclusion of small regenerating understory trees (R2 = 0.42, NRMSE or %RMSE = 30.5%) provides a quantifiable improvement in total estimated AGB compared to using only large woody canopy trees (R2 = 0.21, NRMSE or %RMSE = 36.6%) when correlating field-based biomass measurements with optical image-derived variables. All analyses show that the inclusion of terrain factors made an important contribution to biomass modeling. This study suggests that for young, open forests where there are many small regenerating trees, the contribution of understory biomass should be taken into consideration to improve total AGB estimation.
Opinion Dynamics with Stubborn Neutrals and Competitive Interactions

This paper investigates a general nonlinear model of opinion dynamics with stubborn neutrals and competitive interactions. Based on Perron-Frobenius property of eventually positive matrices and LaSalle invariance principle, we conduct a comprehensive theoretical analysis of the generalized nonlinear opinion dynamics. We obtain some sufficient conditions such that the states of all agents converge into the subspace spanned by the right positive eigenvector of an eventually positive matrix. Finally, a numerical example is given to illustrate obtained results.
Multilayer vehicle classification integrated with single frame optimized object detection framework using CNN based deep learning architecture

Here we have rendered a functional and architectural model of a system that assists the driver of a vehicle to detect, identify and track objects while driving. The objects detected include vehicle type as well as common on-road objects such as pedestrians. Layer structure for the system involves the design of a state-of-the-art deep learning classifier using a novel database for obtaining higher classification accuracy and another layer consisting of a single-frame object detection method to make the system more robust while limiting the processing time involved. Sub-systems integrated to facilitate the driver with relevant real-time information about his driving umwelt include vehicle identifier, number plate recognition system and creation of database consisting of collected information along with time-stamp. Performance degradation under various ambient conditions and variable environments with various synthetic noises being introduced in the video frames have been studied. Trade-off between speed and accuracy of a state-of-the-art real-time detection system implemented on various processing platforms is studied. Layers of deep learning classifier were trained using an optimized dataset consisting of static and dynamic images of vehicles to yield suitable prediction accuracy and this was combined with a system pre-trained on COCO dataset for YOLO. This helped complete the Intelligent Driver Assistant System. This paper also includes the implementation of real-time object detection on a single board computer. This concept can be tapped to create compact and portable driver assistant systems.
Programmable Nanophotonics for Computation

As Moore's law and Dennard scaling come to an end, new devices and computing architectures are being explored. The development of computing hardware designed specifically for machine learning has prompted explorations into both. While silicon photonics is typically viewed as a communications platform, it can serve as a particularly attractive computing platform for specific kinds of problems, including machine learning. Here, we will discuss our work towards realizing photonic matrix processors.
Low interconnection loss and low-cost approach for Antenna in Package of 77GHz Automotive Radar applications

This work provides a low-cost Antenna in package (AiP) implemented on a Advanced single sided substrates (aS3 package) for 77 GHz automotive radar applications. This work demonstrates firstly the 77GHz losses of the transition from chip to package among flip-chip ball grid array (FCBGA) package, flip chip chip scale (FCCSP) package, Fan-out WLP, and aS3 package. Although Fan-out WLP can minimizes 77-GHz losses of transition from chip to package plus package to PCB, low-cost solutions for 77 GHz AiP are importance opportunity for automotive radar applications. The provided AiP solution on aS3 package has low losses of transition from chip to package and the performance meets the requirements of 77 GHz automotive radar systems. aS3 package uses low-cost substrate and lower cost assembly process than Fan-out WLP. This work proposes a low-cost 77GHz AiP solution, and it is suitable for automotive radar applications.
Adaptive Force Reflecting Control for Bilateral Teleoperation System Under Asymmetric Time-Varying Delays

In this paper, the force reflection control problem and position synchronization problem are simultaneously studied for a teleoperation system subjected to asymmetric time-varying delays. In virtue of the prescribed performance control technology, both steady-state and transient-state performance are taken into consideration. The so-called extended state observer is employed to obtain the unmeasured time delay information. In addition, the stability analysis is carried out for the overall teleoperation system. Simulation results have been provided to validate the nrnnosed force reflectlno strateov,
Current Reference Projection Method for Securing Micro-Robot Control Bandwidth Under Voltage Limit Condition

_ This paper proposes a current reference projection method of magnetic manipulation system that controls a magnetized agent, so-called micro-robot. This method reduces the current variances between current and target current based on the actuation matrix characteristic. Reducing the current variation also reduces the settling time of magnetic field and gradient in magnetic manipulation system under voltage limitation. Because magnetic field control directly affects micro-robot control, the proposed method has an advantage in terms of securing micro-robot control bandwidth under voltage limit condition. This paper also discusses efficiency improvement of the proposed method in steady state. The target current of each coil is calculated on PC and controlled by independent full bridge inverters. The advantage of the proposed method was verified by simulation results.
FROM BIRTH TO ADULTHOOD: ANTHROPOMETRIC TRAJECTORIES AND THEIR IMPLICATIONS FOR CHRONIC DISEASES IN GUATEMALA

Abstract The objective of this paper was to study nutritional status and growth, as measured by height and weight, over the life course and their connection with chronic diseases in Guatemala, a country with high levels of child undernutrition and adult overnutrition, using data from the Institute of Nutrition of Central America and Panama (INCAP) Nutrition Trial Cohort study. The study sample comprised a birth cohort of 1570 individuals who had data in the original 1969–1977 survey as well as the 2002–2004 follow-up, allowing for an analysis of the nutritional transition from childhood to adulthood. The associations between childhood and adulthood anthropometrics were analysed, and the links of these with chronic disease indicators were assessed using multiple regression analysis and structural equation modelling. Moving upwards in nutritional status from childhood to adulthood was observed frequently in the study population. Unlike sex and place of residence, early anthropometrics were not generally found to be associated with adult body mass index (BMI). However, direct relationships were found between childhood nutritional status and growth and adulthood high-density lipoprotein (HDL) cholesterol, triglycerides and fasting blood glucose. Furthermore, these relationships were not mediated by BMI. The findings were not sensitive to the metric of childhood anthropometrics, as the use of length-for-age, weight-for-age and weight-for-length all resulted in similar conclusions. These relationships demonstrate the importance of early childhood conditions for later-life outcomes. However, the lack of such relationships for blood pressure suggests that the biological links between childhood anthropometrics and various chronic diseases might vary.
Healthcare Event and Activity Logging

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals’ identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean.
Left ventricular reverse remodeling prediction in non-ischemic cardiomyopathy: present and perspectives

The prognosis of Non-Ischemic Cardiomyopathy (NICM) has strongly improved over the last decades, thanks to the increasing knowledge in the disease pathophysiology and the subsequent development of new drugs and devices (i.e. Implanted Cardioverter Defibrillator [ICD] and Cardiac Resynchronization Therapy [CRT]) [1–3]. Left Ventricular Reverse Remodeling (LVRR), thought to represent mostly the response to optimal medical treatment or to CRT, emerged as a milestone process on the road to achieve better outcomes in NICM [4]. LVRR is usually defined as a Left Ventricular Ejection Fraction (LVEF) increase ≥10 points or its normalization (≥50%) associated with an indexed Left Ventricular Diameter decrease ≥10% or its normalization (≤33 mm/m2). By using this definition, in the populations so far examined, the average rate of LVRR reaches approximately 40% [4]. Despite such achievements, defining prognosis, mostly in the early phases of the disease, and therefore assessing the best management for NICM patients, continue to be arduous in clinical practice. This is proved by the fact that NICM is still the first cause of cardiac transplant worldwide and that some patients are still projected to a poor outcome, often in the short term. Given this, it clearly emerges that the modern approach to this disease and particularly to LVRR prediction must be implemented [1].
RBF-neural network adaptive control of mobile manipulator

The mobile manipulators have attracted a lot of interest in the last few decades. The accurate and reliable control of them seems to be an essential and important requirement for many communication, sensing and control applications. However, some key issues exist that restrict the applications: the presence of external disturbances as well as the model dynamic uncertainties greatly increase the difficulty when we perform the designing of a controller for the mobile manipulator. Aimed at easy the suffering in designing the controller in practice and improve the robot performance, in this paper, an intelligent robust controller based on neural network is proposed for the coordinated control of a mobile manipulator. This method does not require an accurate model of the robot. The unknown dynamic parameters of the mobile platform and the manipulator are identified and compensated in closed-loop control using RBF (Radial Basis Function) neural network. The output errors due to the disturbances can be completely eliminated by this method. The weighting matrices, centers and widths of the RBF structure in the proposed method can be updated on-line. Simulations are presented to show the effectiveness of the presented method.
A Practical Degradation Based Method to Predict Long-Term Moisture Incursion and Color Change in High Power LEDs

The effect of relative humidity on LEDs and how the moisture incursion is associated to the color shift is studied. This paper proposes a different approach to describe the lumen degradation of LEDs due to the long-term effects of humidity. Using the lumen degradation data of different types of LEDs under varying conditions of relative humidity, a humidity based degradation model (HBDM) is developed. A practical estimation method from the degradation behaviour is proposed to quantitatively gauge the effect of moisture incursion by means of a humidity index. This index demonstrates a high correlation with the color shift indicated by the LED's yellow to blue output intensity ratio. Physical analyses of the LEDs provide a qualitative validation of the model, which provides good accuracy with longer periods of moisture exposure. The results demonstrate that the HBDM is an effective indicator to predict the extent of the long-term impact of humidity and associated relative color shift.
Predicting Freezing of Gait in Parkinsons Disease Patients Using Machine Learning

Freezing of gait (FoG) is one of the most alarming motor symptoms of Parkinson's disease, most commonly experienced by subjects who have suffered from the disease for a long period of time. Furthermore, freezing of gait can lead to falls and may result to nursing home admissions which can negatively affect the quality of life for patients, in addition to raising a broader set of socioeconomic consequences. In this research, machine learning algorithms are utilised as means of identifying the freezing of gait event prior to its onset. An accelerometer time series dataset containing 237 individual Freezing of Gait events over 8 patients was considered, from which features were extracted and presented to 7 machine learning classifiers. Our simulation results indicated that machine learning algorithms are powerful tools for the early prediction of freezing of gait, capable of obtaining high sensitivity and specificity for the identification of the onset of Freezing of Gait. Support Vector Machines with Polynomial kernel achieved the highest performance in comparison with the benchmarked techniques. The classification algorithm was applied to 5 second windows using 18 features, obtaining balanced accuracies (the mean value of sensitivity and specificity) of 91 %, 90%, and 82% over the Walk, FoG and Transition classes, respectively.
Designing Multi-Layer Polymeric Nanocomposites for EM Shielding in the X-Band

The past two decades have witnessed rapid progress in the development and application of polymers reinforced with nano-scale constituents (on the order of 100 nm), popularly designated as nanocomposites. The present research work focuses primarily on the EM shielding performance of poly(methyl methacrylate) - multi-walled carbon nanotubes (PMMA - MWCNT) nanocomposites in the electromagnetically polluted X-band. The produced thin polymeric nanocomposite films (on the order of tens to hundreds of micrometers) demonstrate a high level of DC conductivity (approx. 30 S/m) at a moderate filler loading of 3 wt. %. The novelty of the content lies in the fact that relevant electromagnetic design parameters were successfully extracted from conducted electromagnetic interference (EMI) SE waveguide measurements on single-layer nanocomposite films. Subsequently, this knowledge was used to design multiple layered materials with varying arrangements of these individual layers to produce nanocomposites with tailored shielding performance. Such design allows the tuning of the two primal EMI shielding mechanisms: reflectivity and absorption, by varying either the conductivity, sequence or thickness of the individual layers. The EM shielding performance of these multi-layer films was subsequently experimentally verified, and was found to be in good agreement with the theoretical design predictions.
PAPR Performance Analysis of SLM with Hadamard Matrix Based Phase Sequence under M-PSK Modulation for Diminishing PAPR of OFDM System

For attaining very high data speed, orthogonal frequency division multiplexing (OFDM) method is used. It provides its advantage in saving bandwidth. Besides its advantage, it has a difficulty of OFDM signal’s peak power to average power ratio (PAPR). In this paper, selective mapping (SLM) procedure is analysed for diminishing PAPR problem while utilizing the phases from Hadamard matrix, which help in choosing phase sequence very easily and henceforth, diminish the problem of PAPR. Simulation results clearly depict better performance of the proposed scheme in comparison with conventional OFDM system under various modulation schemes and phase sequences.
A Survey on Amphibious Robots

In recent years, amphibious robots are becoming one of the research hotspots, due to their unique advantages of strong environmental adaptability and wide application prospect, which make them have great research value. This paper introduces the research status of amphibious robots mainly from the aspects of bionic mechanisms, motion modes and characteristics. Specifically, merits and drawbacks of some selected representative robots are outlined. In addition, existing technical problems and difficulties in the research of amphibious robots are analyzed. Then, because triphibious robots have the highest research value, development prospects of this kind of robots are discussed with the aim to provide valuable information for the research of triphibious robots. Finally, assumptions and future work are proposed for the research of triphibious robots.
Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game

From the diversity of applications of Network Science, in this Opinion Paper we are concerned about its potential to analyze one of the most extended group sports: Football (soccer in U.S. terminology). As we will see, Network Science allows addressing different aspects of the team organization and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences "can not be analyzed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole" or, in the classical words of after-match interviews "it's not just me, it's the team".