Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable Approach for 360° Feedback in Cooperative Learning

In this paper, we present a standard-based methodology to data-mine and eventually compute the results from 360° feedback surveys. We pay special attention to its suitability in the educational and pedagogical sphere, where very little research efforts have been accomplished. A 360° feedback study (also known as multi-rater feedback) provides a holistic view of an appraisee’s performance based on the information and opinions gathered from raters about the person being evaluated. An evaluator or appraiser can be any peer, coworker, subordinate, supervisor, customer, classmate, and so on and even the appraisee himself/herself. This type of surveys is usually carried out in big companies/institutions and by specialized human resources departments. The computer tools normally used for the processing of the data generated by these studies are based on simple spreadsheets and ad hoc scripts backed by untapped databases. In this context, we advocate the use of a refined, standardized, and straightforward computing environment based on stored procedures. The Persistent Stored Module and the stored procedures there defined are part of the Structured Query Language ISO norm, and they can be leveraged to perform complex calculations without the need of developing external software or requiring extra network transactions. This approach can also be transferred to general 360° appraising studies and, with special emphasis, to those performed in academic levels. As a testbed, the presented techniques have been put into practice in a multi-rater evaluation experience executed with over 240 high-school students. The technical and pedagogical lessons learned in this initial test can be eventually exported to the three main academic levels in a network of educational centers with more than 44k scholars enrolled. The inclusion of such assessment actions in educational environments enables the fostering of the novel pattern of cooperative learning, as it is also highlighted in this paper.
PCANet-Based Convolutional Neural Network Architecture for a Vehicle Model Recognition System

Vehicle model recognition plays a crucial role in intelligent transportation systems. Most of the existing vehicle model recognition methods focus on locating a large global feature or extracting more than one local subordinate-level feature from a vehicle image. In this paper, we propose the principal component analysis network-based convolutional neural network (PCNN) and pinpoint only one discriminative local feature of a vehicle, which is the vehicle headlamp, for vehicle model recognition. The proposed model eliminates the need for locating and segmenting the headlamp precisely. In particular, PCNN ascertains the effectiveness of both principal component analysis and CNN in extracting hierarchical features from a vehicle headlamp image and also reducing the computational complexity of the traditional CNN system. To further enhance the training procedure while still keeping the discriminative property of the network, the fully connected layer is updated by backpropagation optimized with stochastic gradient descent. The proposed method is validated using a data set that comprises 13 300 training images and 2660 testing images, respectively. The model is robust against various distortions. Experiments show that PCNN outperforms state-of-the-art techniques with an average accuracy of 99.51% over 38 vehicle makes and models using the PLUS data set. In addition, the effectiveness of the proposed method is also validated using the public CompCars data set, achieving 89.83% accuracy over 357 vehicle models.
Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. However, biopsies are highly invasive, and patients with benign nodules may undergo many unnecessary biopsies. To prevent this, an imaging diagnosis with high classification accuracy is essential. In this study, we investigate the automated classification of pulmonary nodules in CT images using a deep convolutional neural network (DCNN). We use generative adversarial networks (GANs) to generate additional images when only small amounts of data are available, which is a common problem in medical research, and evaluate whether the classification accuracy is improved by generating a large amount of new pulmonary nodule images using the GAN. Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. The benign nodules assessed in this study are difficult for radiologists to differentiate because they cannot be rejected as being malignant. A volume of interest centered on the pulmonary nodule is extracted from the CT images, and further images are created using axial sections and augmented data. The DCNN is trained using nodule images generated by the GAN and then fine-tuned using the actual nodule images to allow the DCNN to distinguish between benign and malignant nodules. This pretraining and fine-tuning process makes it possible to distinguish 66.7% of benign nodules and 93.9% of malignant nodules. These results indicate that the proposed method improves the classification accuracy by approximately 20% in comparison with training using only the original images.
Accurate Prediction of Sintering Temperature in Rotary Kiln Based on Phase Space Reconstruction

The Sintering Temperature (ST) in the burning zone of the alumina rotary kiln determines the quality of the alumina clinker directly and the energy consumption. What is more, the ST is a key controlled parameter to the alumina rotary kiln. In this paper, a kind of Phase-Space-Reconstruction-based (PSR-based) prediction methods are proposed to accurately predict the ST. After comparative experiments with three PSR-based models, it is found that the PSR-based weight first-order approximation method not only has the highest predictive accuracy but also has strong enough robustness.
FOD Detection using DenseNet with Focal Loss of Object Samples for Airport Runway

The foreign object debris (FOD) on the airport runways is a serious threat to aircraft taxiing, taking off and landing. The installation of cameras on the side of the track to automatically monitor the FOD is very important for improving the safety of the runway. This paper proposes a FOD detection method based on Convolutional Neural Network (CNN) for small-scale FOD. This method employs FasterR-CNN framework to generate candidate regions for the input image. This paper uses DenseNet instead of the traditional VGGl6Net for feature extraction, which can greatly reduce the network parameters and is beneficial to small-scale FOD detection. This paper also modifies the loss function of classification in RPN and uses Focal Loss to optimize the weights of positive and negative samples, so that the training results are focused on FOD samples with high difficulty in classification in dense samples.Experiments show that our proposed method has good performance in real-time, detection accuracy and anti-interference. The airport runway FOD image dataset mainly includes four types of objects (small steel balls, metal nuts, large screws, and small screws). Compared with the classical FasterR-CNN, our method achieves 95.60% FOD detection accuracy with an increase of 15.91%, while the detection speed is also more than doubled.
Bidirectional Fuzzy Brain Emotional Learning Control for Aerial Robots

This paper proposes a Bidirectional Fuzzy Brain Emotional Learning (BFBEL) control system to control Aerial Robots. The proposed controller is based on the emotional and logical processing of the brain. The proposed control system merges fuzzy inference and a bidirectional brain emotional learning algorithm. The Bidirectional Fuzzy Brain Emotional Learning (BFBEL) control can learn from scratch and adapt rapidly in real-time to control the system without much prior information. The proposed controller is tested against simulations of both a 1-Degree-Of-Freedom (DOF) flapping wing and a 6DOF flapping wing model and successfully implemented on a 1DOF flapping wing experiment which showcases the learning and adaptation capability in a real-time environment.
Incorporating Dynamic Exploration Strategy for RNA Design

Ribonucleic acid (RNA) design is a problem that has been shown to be NP-Hard. This is so because checking the quality of a solution requires an expensive fold operation. Getting a deterministic solution is unrealistic, so a heuristic approach seems to be the best strategy. In our previous research, we have designed an RNA design algorithm based on the simulated annealing paradigm. In this paper, we are proposing a novel Dynamic Exploration Strategy (DES) in an effort to explore a more diverse design space without increasing the run-time. We investigated this method with nine different sets of parameters. These parameters control the influence the size of our mutations, and the number of candidate solutions per evaluation step that we generate. To verify the DES approach, we tested our algorithm on two very diverse datasets (Rfam and Eterna100). We found that DES can enable us to find high quality sequences in a shorter amount of time than the previous approach.
A Lag Angle Compensation Strategy of Phase Current for High-Speed BLDC Motors

For brushless dc motor operating at ideal commutation point, the angle of the phase current vector will lag behind that of the back electromotive force (EMF) vector due to the motor phase inductance. The lag angle increases the root-mean-square (rms) value of phase current under a certain average torque output, which results in more heat and less efficiency, especially at high rotational velocity. The principle that the angle of ideal current vector is equal to that of the back EMF vector at the center of a conduction sector is analyzed. A novel advance compensation method for commutation point is proposed based on the principle. The advance angle is calculated in real time according to the phase current, the inductance, and the flux linkage established by the magnet of the motor. By conducting the voltage vector of voltage source inverter before the ideal commutation point in advance, the lag of the phase current would be eliminated. Thereby, the rms value of the phase current is reduced. Finally, both simulation and experiments verify the effectiveness of the proposed method.
The Interaction between Coffee: Caffeine Consumption, UCP2 Gene Variation, and Adiposity in Adults—A Cross-Sectional Study

Background Coffee is suggested as an alternative option for weight loss but the relationship between coffee consumption and adiposity in population-based studies is still controversial. Therefore, this study was aimed at evaluating the relationship between coffee intake and adiposity in adults and to test whether uncoupling protein 2 (UCP2) gene variation was able to affect this relationship. Methods This was a cross-sectional study conducted in male and female adults living in the urban area of Yogyakarta, Indonesia. Adiposity was determined based on body weight, body mass index (BMI), percent body fat, and waist and hip circumference. Data on coffee consumption and other dietary components were collected using a semiquantitative food frequency questionnaire along with other caffeine-containing beverages such as tea, chocolate, and other beverages. The −866 G/A UCP2 gene variation was analyzed using polymerase chain reaction-restriction fragment length polymorphism. The correlation between coffee intake and adiposity was tested using linear regression test with adjustment for sex, age, energy intake, table sugar intake, and total caffeine intake. Results In all subjects, coffee intake was inversely correlated with body weight (β = −0.122, p=0.028), BMI (β = −0.157, p=0.005), and body fat (β = −0.135, p=0.009). In subjects with AA + GA genotypes, coffee intake was inversely correlated with body weight (β = −0.155, p=0.027), BMI (β = −0.179, p=0.010), and body fat (β = −0.148, p=0.021). By contrast, in subjects with GG genotype, coffee intake was not correlated with body weight (β = −0.017, p=0.822), BMI (β = −0.068, p=0.377), and body fat (β = −0.047, p=0.504). Conclusion We showed that coffee intake was negatively correlated with adiposity, and this was independent of total caffeine intake. Additionally, we showed that the −866 G/A UCP2 gene variation influences the relationship between coffee intake and adiposity.
Evaluation of Size and Location of a Mental Foramen in the Polish Population Using Cone-Beam Computed Tomography

Introduction The mental foramen (MF) is a bilateral opening localized on an anterior surface of the mandible. A precise location as well as well-defined shape, size, and number of the MF is crucial for different clinical dental procedures. The aim of this study was to determine a size and location of the MF in relation to the lower teeth using the cone-beam computed tomography (CBCT) study. Material and Methods In a group of 201 patients (106 males and 95 females) the CBCT images were performed using the GX CB-500 device (Gendex, USA). Results No significant differences in values of the horizontal (H) and vertical (V) diameters as well as the H:V ratio on both sides in relation to the age of participants were found. In males both average values of a horizontal diameter (p=0.031) and vertical diameter (p=0.001) were significantly higher on the right side than in the female subgroup, whereas on the left side only an average value of a vertical diameter was significantly higher in men (p=0.006) in comparison to women. Moreover, the H:V ratio was significantly lower in males on the left side (p=0.032). There were no significant relationships between age and gender of the patients (p>0.05) and the type of mental foramen on the right and left sides. Conclusions The application of the CBCT study enabled a precise determination of the shape, size, and position of the mental foramen in relation to the neighboring anatomical structures on a representative group of the Polish patients. The results obtained may contribute to guidelines for dental procedures including anesthesia of the mental nerve and endodontic, implantology, and dental surgery with regard to the location of mental foramen depending on the sex and age of patients.
Ensemble of Technical Analysis and Machine Learning for Market Trend Prediction

Over the last twenty years, researchers and practitioners have attempted in many ways to effectively predict market trends. Till date, however, no satisfactory solution has been found. Many approaches have been applied to predict market trends, from technical analysis to fundamental analysis passing through sentiment analysis. A promising research direction is to exploit market technical indicators together with market sentiments extracted from social media for predicting market directional movements. In this paper, we propose a new approach that leverages technical analysis to predict market directional movements. In particular, we aim to predict the directional movement of the NASDAQ’s most capitalized stocks by solving a classification problem. The results on real-world data show that our proposal achieves interesting performance when predicting the market directional movements. This work focuses on forecasting a portfolio of different stocks, instead of concentrating on a single stock which most of the works in this field do. Furthermore, the proposed model is able to solve the issue of skewed classes through the use of appropriate data balancing techniques.
Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases

Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers.
Consensus of Second-Order Multi-Agent Systems by Event-Triggered Control

In this paper, event-triggered consensus problem of second-order multi-agent systems is analyzed. We develop a novel event-triggered controller for each agent, which only needs to be updated at its own triggering time instants. With the proposed controller, the consensus of the studied multi-agent systems is achieved exponentially, and Zeno behavior is strictly excluded for each agent. Finally, a simulation example is presented to verify the feasibility of the proposed control strategy.
Reversible Data Hiding in Encrypted Images with Two-MSB Prediction

In recent years, reversible data hiding in encrypted images (RDHEI) that embeds additional data into the encrypted image content has received more and more attention. In previous RDHEI methods, there is no one considering the correlation between neighboring pixels. In this paper, to better explore the correlation between neighbor pixels, a novel reversible data hiding method in encrypted images with two-MSB (MSB and second MSB) prediction is proposed. After analyzing the original image content and detecting all the possible prediction errors, we encrypt the original image content using the bitwise exclusive-or (XOR) operation and highlight prediction error based on the error location binary map. Finally, all available pixels are embedded with additional data by two-MSB substitution. In our method, reversibility, separability and error-free data extraction are realized. The experimental results show that the proposed method providing a higher embedding rate and better quality of directly decrypted image compared with other start-of-the-art methods.
Knowledge and Experience of Neonatal Resuscitation among Midwives in Tamale

Background. Efforts to reduce under-five mortality across the globe are being hindered by a disproportionately high rate of neonatal deaths. About a quarter of these neonatal deaths are caused by birth asphyxia. Available evidence shows that effective neonatal resuscitation delivered by providers skilled in and knowledgeable about neonatal resuscitation can significantly reduce neonatal mortality rates. Objectives. This cross-sectional study was conducted to determine knowledge and experience in neonatal resuscitation among midwives in Tamale, and the factors associated with their knowledge on neonatal resuscitation. Methods. This was a cross-sectional study involving all midwives practicing in three large hospitals in Tamale. We developed a questionnaire to collect data on demographic characteristics of participants, and their knowledge and experience in neonatal resuscitation. We used the Statistical Package for Social Sciences (SPSS), version 21, to analyse the data. Demographic characteristics of participants were summarized using descriptive statistics. Pearson's correlation was used to determine associations between knowledge and some selected demographic features, while the one-way ANOVA was conducted to determine differences in level of knowledge based on the demographic features. Results. 98.1% of the participants in this study had insufficient knowledge on neonatal resuscitation. Midwives at the Tamale Central Hospital demonstrated a statistically significantly higher level of knowledge (24.67 ± 2.79, p = .014), compared to those at the Tamale Teaching Hospital (22.92 ± 4.56, p = .028) and Tamale West Hospital (21.50 ± 6.24, p = .021). Those who had a first-degree qualification in midwifery and those with a Post-NAC/NAP midwifery certificate had a statistically significantly higher knowledge than those with a diploma in midwifery. Training in neonatal resuscitation was associated with more knowledge in neonatal resuscitation (r(158) = . 195, p = . 013). In terms of experience, 55% of the participants in this study were not experienced in performing neonatal resuscitation. There were no differences in their level of experience based on their academic qualification, work place, and years of practice as a midwife. Conclusion. Considering the generally low level of knowledge and experience of midwives in neonatal resuscitation as discovered in this study, there is an urgent need for government to provide more opportunities for all practicing midwives to be trained in this important lifesaving skill.
Phytochemical Constituents and Antioxidant Activity of Sweet Basil (Ocimum basilicum L.) Essential Oil on Ground Beef from Boran and Nguni Cattle

The global meat industry is characterised by a growing interest in natural preservative additives. This study determined the effect of sweet basil (Ocimum basilicum L.) essential oil (SBEO) on colour and lipid oxidation in minced beef. The phytoconstituents of SBEO were analyzed by gas chromatography mass spectrometry. Thereafter, minced beef samples from Nguni and Boran cattle were treated with either no additives (control, C) or SBEO added at 2% (SB2), 4% (SB4), or 6% (SB6). The meat samples were aerobically packaged and stored (4 ± 1°C) for seven days for measurement of lightness (L⁎), redness (a⁎), yellowness (b⁎), hue, chroma, and lipid oxidation (acid-reactive substances, TBARS) on days 0, 4, and 7. Thirty-two bioactive compounds with reported antioxidant and antimicrobial and activities were identified in SBEO, including Estragole (41.40%), 1, 6-Octadien-3-ol, 3,7-dimethyl (29.49%), and trans-. alpha. -Bergamotene (5.32%). On days 0, 4, and 7, SB2, SB4, and SB6 had higher (P < 0.05) L⁎, a⁎, b⁎, hue, and chroma values; and on days 0 and 4 TBARS were lower (P < 0.05) in SB2 and SB4 than C and SB6. The addition of 2% and 4% SBEO improved colour and lipid oxidative stability, demonstrating potential for its use as a natural antioxidant additive in meat.