Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tool for Modelling Business Behaviour Using Decision Tables

Decision tables have been recently recognised as an effective technique to model business behaviour. The increasing adoption of decision tables in this context requires appropriate end-user support. Although several tools have been developed using decision tables, few have been targeted to business end users. This paper fulfils this gap by proposing a tool supporting end users to capture and model business behaviour. The proposed tool integrates decision tables for capturing actionable knowledge using ontology technology. The tool is validated in two case studies.
An Analytical Comparison of MU-MIMO and Single User Transmissions in IEEE 802.11ac

IEEE 802.11ac defines Multi-User MIMO (MU-MIMO) in the downlink that allows the Access Point (AP) to transmit multiple spatial streams to different receivers, simultaneously. However, MU-MIMO requires a periodic channel calibration which incurs a significant overhead. On the other hand, a Single User (SU) transmission may be used in both downlink and uplink. It allows the sender to transmit one (i.e. SISO) or multiple streams (i.e. SU-MIMO) to the same receiver. We note that SU transmissions do not require the channel calibration. In this paper, we compare the throughput of SISO, SU-MIMO and MU-MIMO, analytically, in an ideal condition (i.e. no frame losses) to understand which technique is more efficient and when. We measure the throughput for different channel widths, data rates, A-MPDU lengths, channel calibration rates and network sizes. Our results show that SU-MIMO is typically more efficient when the receiver supports the same number of spatial streams than the AP. But if the receivers support a single stream, MU-MIMO improves the network efficiency for a limited number of stations. Besides, the MU-MIMO scalability depends on the channel calibration rate and the number of receivers.
The protection knowledge and performance of Radiographers in some hospitals of Ahvaz County

Background: Diagnostic radiology is the process of obtaining a high-quality image while minimizing the doses received by patients. Radiographers’ knowledge of protection and safety principles and their correct application plays a significant role in radiation protection. Hence, this study aims to evaluate the radiographers’ protection and safety knowledge, attitude and practice in the radiology wards at several hospitals in Ahvaz County. Materials and Methods: This study is a descriptive and cross sectional one conducted using an observation checklist and a scientific questionnaire, respectively by the researcher and radiographers to evaluate the protection and safety practice and knowledge of radiographers in some selected hospitals in Ahvaz County. The collected data were analyzed, using the SPSS version 20, through descriptive statistics, independent t -test, Pearson correlation test and analysis of variance. Results: A significant correlation exists between r adiographers’ educational level and their protection and safety knowledge and practice. The results shows a significant difference between the protection and safety knowledge among radiographers in the selected hospitals as well as in their attitude and pr actice. It was also revealed that there exist a shortage of lead apron (29%) and a low level of its use even when available; the gonad and thyroid shield and lead partition for mobile radiology were not used at all. Conclusion: The present study confirms the need to highlight protection and safety principles in the departments of radiology to ensure the safety of radiographers and patients. In this regard, supplying protection equipment and holding courses on radiation protecti on are useful.
High Performance and Highly Robust AlN/GaN HEMTs for Millimeter-Wave Operation

We report on a 3 nm AlN/GaN HEMT technology for millimeter-wave applications. Electrical characteristics for a 110 nm gate length show a maximum drain current density of 1.2 A/mm, an excellent electron confinement with a low leakage current below <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{A}$ </tex-math></inline-formula>/mm, a high breakdown voltage and a F<sub>T</sub>/F<sub>max</sub> of 63/300 GHz at a drain voltage of 20V. Despite residual trapping effects, state of the art large signal characteristics at 40 GHz and 94 GHz are achieved. For instance, an outstanding power added efficiency of 65% has been reached at V<sub>DS</sub> = 10V in pulsed mode at 40 GHz. Also, an output power density of 8.3 W/mm at V<sub>DS</sub> = 40V is obtained associated to a power added efficiency of 50%. At 94 GHz, a record CW output power density for Ga-polar GaN transistors has been reached with 4 W/mm. Additionally, room temperature preliminary robustness assessment at 40 GHz has been performed at V<sub>DS</sub> = 20V. 24 hours RF monitoring showed no degradation during and after the test.
Performance analysis of OFDM-IM over Faded Shadowing Channel with co-channel interference

Orthogonal frequency division multiplexing with Index Modulation (OFDM-IM) has been used to transmit extra bits by indexing subcarriers using the ON-OFF keying modulation. In this paper, the closed-form of average bit error probability (ABEP) for BPSK and M-ary QAM modulations with OFDM-IM are derived in the absence and presence of co-channel interference (CCI) over composite Nakagami-m and Gamma (NG) shadowing channel. Simple closed-form expressions are introduced by using the Meijer G function. The theoretical results are validated through comparisons with existing research studies.
Urban Commerce Distribution Analysis Based on Street View and Deep Learning

Urban commerce and its distribution have always been an important part of urban research. However, most previous studies were based on statistical data and did not reflect real street experience. Thanks to the Street View image and deep learning technology, researchers are able to carry out large scale studies from real human visual experience. In this article, we aim at sensing the commercial spaces in cities. In order to achieve this ultimate goal, deep learning is applied to process the raw data of Street View image. We disassemble the goal into three tasks: firstly, obtaining all the Street View images in a specific area; then classifying the Street View images according to the commercial facilities in it; and finally creating a visualization of the detected data into a map. For the first task, we get the road network coordinate information from the openstreetmap (OSM) website, set the sampling point on the road, and then download the Street View images of the sampling points’ coordinate through the API provided by Baidumap. For the second task, we adopt a two-level learning strategy rather than directly using Deep Convolutional Neural Network for classification. For the final task, we choose the heat map as the expression of the results and draw the map by the existing GIS software. Furthermore, the results from this study can be conveniently combined with other data because of the use of street-network-based data structure. An application of this method combines with street-network data, the calculation of a city’s 15-minute commercial service circle coverage is also shown in this study.
Glycosylated hemoglobin and fasting blood glucose levels in type 2 diabetes mellitus

Background: Diabetes is a growing public health problem throughout the world. It is now among the five leading causes of death to disease in most countries. As a consequence of hyperglycemia in diabetes every tissue and organ of the body undergoes biochemical and structural alterations which accounts for major diabetes complications. Our aim was to determine the levels of glycosylated hemoglobin (HbA 1C ) and fasting blood glucose (FBG) in patients with type 2 diabetes mellitus and to compare with healthy controls and also the study of correlation between Fasting blood glucose and HbA 1C levels in diabetic patients. Material and Methods: In this study total 80 subjects were included above 40 years of age. 40 clinically diagnosed type 2 diabetic patients and 40 normal subjects were recruited as control. HbA1 C and fasting blood glucose levels were measured by the methods of Parker K. M. et al.and Trinder P respectively. Results: The mean value of HbA 1C in type 2 diabetic patients was significantly higher than in the controls. The mean fasting blood glucose levels were significantly higher in type 2 diabetics when compared with controls and the significant correlation was observed between levels of fasting blood glucose and HbA 1c in diabetic patients. Conclusions: HbA 1c can be used as a monitor of diabetic control and thus useful in assessing the status and the therapeutic progress of diabetes mellitus.
A Fast Local Mode Decision for the HEVC Intra Prediction Based on Direction Detection

This work presents a fast local mode decision for the HEVC intra prediction based on a direction detection algorithm first proposed in the Daala video format and currently used in the AV1 deringing filter. The main objective is to reduce the number of intra candidates entering the very expensive RDO loop by locally detecting the dominant edge direction of the original block, without the need of computing any of the intra prediction modes and associated RD costs. The proposed method was implemented on the latest HEVC reference encoder (HM 16.20), replacing the original local mode decision by the algorithm proposed in this paper. Experiments under the HEVC common test conditions, including UHD 4K (3840x2160 pixels) test sequences, showed, on average, 28.2% encoding time reduction, at a cost of 1.0% BD-BR (YUV) increase. Specifically for UHD 4K sequences, the experiments showed, on average, 30.0% encoding time reduction, at cost of 0.9% BD-BR (YUV) increase.
Practice nurses and computing: some evidence on utilization, training, and attitudes to computer use

Seventy-seven practice nurses in the Portsmouth and Worcestershire areas were surveyed by postal questionnaire about their experience with, and attitudes to, using computers in general practice nursing. Almost all (98%) of the respondents worked in a computerized general practice and all reported having a positive attitude towards the clinical and administrative use of computers. Some 84% of practice nurses had a computer terminal where they saw patients and over 70% used the computer to gain access to clinical information. However, over three-quarters (79%) of respondents felt that their training on computers was either missing or inadequate. Some 74% of the nurses surveyed who were in post when the computers were introduced to their practice were either hardly consulted prior to installation, or not at all. Despite positive feelings about computerization, practice nurses appear to be getting insufficient or inadequate training and support to fully utilize the technology.
Phytochemicals and acute toxicity of Moringa oleifera roots in mice

The phytochemicals used by plants to protect themselves against predators in Moringa oleifera roots were qualitatively identified in the aqueous and ethanol extracts. Its acute toxicity in 24 h was evaluated in Swiss albino mice. M. oleifera, a native plant of the sub-Himalayan tracts of India, Pakistan, Bangladesh and Afghanistan is used in folk medicine. It is claimed to have nutritional, medicinal, socio-economic and industrial values. Many individuals and families consume the roots for their medicinal properties. Despite wide use the roots, the phytochemicals and toxicity profile are not well documented. This study set out to determine the phytochemicals and acute toxicity of M. oleifera roots in mice. The roots were harvested during dry season and air dried. Serial extractions using ether, ethanol and water were done. The harvested phytochemicals were qualitatively identified using standard chemicals procedures. The phytochemicals identified were: gallic tannins, catechol tennins, steroids and triterponoids, saponins, anthraquinones, alkaloids, and reducing sugars. Acute toxicity was determined by giving a single oral dose to Swiss albino mice and observed for 24 h. The LD50 was calculated using the probit tables. The LD50 of ethanol extract was 17.8 g/kg and that of aqueous extract was 15.9 g/kg. In conclusion, M. oleifera roots contain protective phytochemicals and are relatively non-toxic when given in a single dose. Key words: Moringa oleifera roots, phytochemicals, medicinal plant extracts, herbal medicine, acute toxicity, mice
Child based factors influencing implementation of Early Childhood Development and Education Curriculum

Young Chidren are the sole consumers of the Early Childhood Development and Education (ECDE) Curriculum implemented. The state of children and the care given to them determine the level of Curriculum implemented. Although the Kenya Education Sector Support Program (KESSP)has tried to provide free material and facilities to ECDE centres and the Government of Kenya training many teachers to enhance ECDE. Public confidence in it is still in question as primary schools doubt the preparedness of children promoted from ECDE. Factors leading to this situation are unknown. The purpose of this study was to establish child based factors influencing implementation of ECDE Curriculum. The study involved 65 ECDE teachers drawn from 32 ECDE centres in Emuhaya District, Kenya selected by simple random technique. It employed descriptive survey design. Data was collected by questionnaire. The study found out that Learners with special needs were not given a special attention instead, they were just taught together with the ‘normal’ children in the same classroom since ECDE teachers lacked training in special needs education for handling children with various special needs. The study recommends that teachers should be trained to handle children with different special needs. Special units for children with special needs should be set up in all ECDE Centres.
MTPA based DTC for Permanent Magnet assisted Synchronous Reluctance Motor for Electric Vehicle application

A new maximum torque per ampere (MTPA) based direct torque control (DTC) method is proposed in this paper for a permanent magnet assisted synchronous reluctance motor (PMaSyRM). The central idea of the proposed control algorithm is to dynamically adjust the value of stator flux reference in accordance with the load torque. The envisaged idea enables reap more benefits from the motor drive mainly under low or no-load conditions. Analytical expression for the stator flux reference needed is presented. In addition, this paper also presents a methodology to determine the core loss component of the PMaSyRM that enables estimation of electrical losses in the motor. Firstly, performance characteristics obtainable from the PMaSyRM using the proposed MTPA based DTC is presented under different conditions. Secondly, they are compared with that obtainable from the conventional DTC method to showcase the superiority of the MTPA based DTC proposed in this paper. This is further examined by considering the Indian Drive Cycle (IDC) and an electric vehicle (EV) model. Results in terms of reduced core losses, stator copper losses and improved motor efficiency etc. are presented in support of the fusion of MTPA with DTC methods for PMaSyRM.
Estimation of a Low-Rank Probability-Tensor from Sample Sub-Tensors via Joint Factorization Minimizing the Kullback-Leibler Divergence

Recently there has been a growing interest in the estimation of the Probability Mass Function (PMF) of discrete random vectors (RVs) from partial observations thereof (namely when observed realizations of the RV are limited to random subsets of its elements). It was shown that under a low-rank assumption on the PMF tensor (and some additional mild conditions), the full tensor can be recovered, e.g., by applying an approximate coupled factorization to empirical estimates of all joint PMFs of subgroups of fixed cardinality larger than two (e.g., triplets). The coupled factorization is based on a Least Squares (LS) fit to the empirically estimated lower-order sub-tensors. In this work we take a different approach by trying to fit the coupled factorization to estimated sub-tensors in the sense of minimizing the Kullback-Leibler divergence (KLD) between the estimated and inferred tensors. We explain why the KLD-based fitting is better-suited than LS-based fitting for the problem of PMF estimation, propose an associated minimization approach and demonstrate some advantages over LS-based fitting in this context using simulation results.
On Multivariate Non-Gaussian Scale Invariance: Fractional Lévy Processes And Wavelet Estimation

In the modern world of “Big Data,” dynamic signals are often multivariate and characterized by joint scale-free dynamics (self-similarity) and non-Gaussianity. In this paper, we examine the performance of joint wavelet eigenanalysis estimation for the Hurst parameters (scaling exponents) of non-Gaussian multivariate processes. We propose a new process called operator fractional Lévy motion (ofLm) as a Lévy-type model for non-Gaussian multivariate self-similarity. Based on large size Monte Carlo simulations of bivariate ofLm with a combination of Gaussian and non-Gaussian marginals, the estimation performance for Hurst parameters is shown to be satisfactory over finite samples.
Low-Complexity Switching Network Design for Hybrid Precoding in mmWave MIMO Systems

This paper deals with the design of a hybrid precoder for millimeter-wave MIMO systems. For the sake of concreteness, we consider an analog processing stage composed of a switching network with analog combining. The main contribution of this work consists on the proposal and evaluation of an optimization procedure based on a smart relaxation. The optimal hybrid precoder under a transmit power constraint is derived, after which, the analog precoding matrix is binarized. After an intuitive reasoning, we note that multiple solutions exist. Nevertheless, the (very) reduced computational complexity of the proposed optimization scheme makes it feasible for realistic implementations. Numerical results are reported to assess the performance of proposed hybrid precoder design.
What Kinds of Robot's Touch Will Match Expressed Emotions?

This study investigated the effects of touch characteristics that change the strength and the naturalness of the emotions perceived by people in human-robot touch interaction with an android robot that has a feminine, human-like appearance. Past studies on human-robot touch interaction focused on understanding what kinds of human touches conveyed emotion to robots, i.e., the robot's touch characteristics that can affect people's perceived emotions received less focus. In this study, we concentrated on three touch characteristics (length, type, and part) based on arousal/valence perspectives, and their effects on the perceived strength/naturalness of a commonly used emotion in human-robot interaction, i.e., happiness, and its counterpart emotion, (i.e., sadness), borrowing Ekman's definitions. Our results showed that the touch length and its type are useful to change the perceived strengths and the naturalness of the expressed emotions based on the arousal/valence perspective, although the touch part did not fit such perspective assumptions. Finally, our results suggest that a brief pat and a longer contact by the fingers are better combinations to express happy and sad emotions with our robot. Since we only used a female android, we discussed future works with a male humanoid robot and/or a robot whose appearance is less humanoid.
Convex Combination of Spline Adaptive Filters

In this paper, we propose an adaptive and convex combination of a recent class of nonlinear adaptive filters in different configurations. The proposed architecture relies on the properties of the adaptive combination of filters which exploits the capabilities of different constituents, thus adaptively providing at least the behavior of the best performing filter. The nonlinear functions involved in the adaptation process are based on spline function interpolation and their shapes can be modified during learning using gradient-based techniques. In addition, we derive a simple form of the adaptation algorithm and present some experimental results that demonstrate the effectiveness of the proposed method.
A Systematic Approach to Identify an Appropriate Classifier for Limited-Sized Data Sets

Data size is a main issue in any data mining application, since limited size data results in a small training set that leads to a poor classification model and therefore a poor classification performance. Although, many real-life applications need a classifier that deals with limited size data sets appropriately. A considerable interest is focused on how to achieve a reasonable classification performance for small data sets. Current works focus on either enhancing classification algorithms or enlarging the data sets, these solutions have limitations such as increasing the computational time, or reaching data sets that do not reflect the actual population of the real data. However, this research looks at the problem from a different angel, it aims to address the data quantity issue by identifying the most appropriate classifier for small data sets using three well-known classifiers which are Decision tree (J48), Support Vector Machine (SVM) and Naïve Bayes. Extensive experiments are conducted to examine the performance in terms of four different measures which are accuracy, f-measure, sensitivity and specificity. We used six small data sets from UCI repository with different attributes and instances sizes. Results revealed that SVM accomplished the best performance along most of the used data sets.
QoS Attributes Prediction with Attention-based LSTM Network for Mobile Services

With the rapid popularization of mobile terminal equipment, the traditional service is developing towards the direction of mobile service composition with high real-time and high reliability. As mobile services and applications continue to be more widespread, it is necessary to accurately predict userside Quality of Service (QoS), to ensure the optimal selection, composition and adaptation of mobile services. The time-varying characteristic of QoS is the commonness of web services. In addition, user s position may change at intervals in mobile service scenario. And QoS varies from place to place due to various external factors. Many existing QoS prediction approaches fail to consider the mobility of users and cannot reflect the correlation between time varying QoS values and some external factors such as user s position. To solve this problem, we use a LSTM based Encoder-Decoder network with attention mechanism to predict the QoS at future time. This model combines the location variability of service users and the fluctuation of QoS in time series. Experiments are conducted on WS-DREAM dataset and results have proved the effectiveness of the proposed method.
Stabilization and Stability Robustness of Coupled Non-Constant Parameter Time Fractional PDEs

This paper considers observer-based output feedback stabilization and stability robustness against small diffusivity perturbations of coupled time fractional partial differential equations (PDEs) with space-dependent (non-constant) parameters. Herein, the plant is equipped with the only available measurement at <inline-formula> <tex-math notation="LaTeX">$x=0$ </tex-math></inline-formula> and actuation at <inline-formula> <tex-math notation="LaTeX">$x=1$ </tex-math></inline-formula>. By backstepping transformation, the well-posedness of the kernel matrix PDE and the observer gains are obtained. Then an output feedback controller is introduced and the Mittag-Leffler stability of the closed-loop system is proved by the fractional Lyapunov method. Robustness analysis of diffusion coefficients uncertainty (with a small perturbation in the diffusion coefficient) is also provided. The output feedback stabilization of the closed-loop system is tested by a numerical example.