Miracle-dz/UniFAR-FTUs huggingface dataset:
Door Access System Using Face Recognition

This is a security system to restrict access to rooms using Raspberry Pi as main controller and implementation of Histogram of Oriented Gradients (HOG) feature extraction method. Upon face detection, the system is able to do face recognition for person identification by matching face embedding of the person face with one that already stored in database. This system also presents a random head-pose challenge to identify a real person from an image and then activate a solenoid to unlock the door. In this experiment, if the person is not wearing any face attributes, this system is tested to have 100% successful detection and recognition rate at 60 cm distance. If the person is wearing mask attribute, this system is tested to have 70% successful rate for detection and 45% successful rate for recognition at 60 cm distance.
A double network structure anti-jamming algorithm based on deep reinforcement learning

At this stage, when dealing with intelligent jammer with environmental cognition, most anti-jamming methods only consider how to maximize avoidance of it. However, the user’s previous waveform and frequency actions have been leaked. As the intelligent jammer continues to learn, the performance of anti-jamming will decline. Existing literature has proposed the idea of hidden anti-jamming. The user uses rational communication action-making to prevent the jammer from obtaining the information of the user so that the jammer cannot target the user for jamming, but its model is relatively simple and cannot cope with complex jamming types. This paper designs a kind of decision related judgment module between jammer and user based on Generative Adversarial Networks (GAN), which uses the environment state received by the user to reversely infer the user’s decision whether to avoid jammer’s sense. Specifically, the neural network is used to fit the environment state under known user information, and calculate the loss between it and the real environment state to evaluate whether the user’s decision is to avoid jammer’s sense. Then, this paper proposes a deep reinforcement learning algorithm with double-network structure, which can deal with various types of complex jamming while ensuring anti-jamming performance. The final simulation results show that the improved deep reinforcement learning algorithm proposed in this paper can improve the anti-jamming performance, achieve hidden anti-jamming, and have a wider range of applications.
Application of BERT+Attention Model in Emotion Recognition of Metizens during Epidemic Period

In 2020, SARS-CoV-2 will affect the hearts of people all over the country, and Weibo will become the representative of people expressing their feelings on the Internet. Traditional emotion dictionary and machine learning methods have poor text emotion recognition effect, while BERT pre-training model is based on bidirectional Transformer model, which can better obtain the emotion expressed by the text and effectively improve the accuracy of the model. On the basis of improving BERT pre-training model, attention mechanism is introduced, and the key features are weighted to make emotion classification more accurate. According to the analysis of emotions expressed by netizens on Weibo during the epidemic, compared with textCNN model, BILSTM model and BILSTM+Attention model, the accuracy rate has increased by 6.25%, 4.69% and 2.67% respectively. The overall performance of this model is the best, and it can effectively recognize text emotion.
New Artificial Intelligence Technology Applied in Automobile Lithium Battery Manufacturing

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, this paper is based on artificial intelligence technology for lithium-ion battery model and parameter identification algorithm, adaptive unscented Kalman filter algorithm and SOC estimation based on battery model fusion Algorithm for research. The simulation results show that the SOC error estimated by the artificial intelligence adaptive Kalman filter method is less than 2.4%, which effectively reduces the impact of unknown interference noise on the battery management system when the electric vehicle is driving. The SOC estimation accuracy is higher than that of the extended Kalman method, and has good robustness.
Evaluation of Systems Current Status by PCA-RBF Neural Network and Novel Fuzzy Intelligence Method

The health and the sustainability of a higher education system are crucial to the prosperity of a nation. It is crucial to effectively evaluate a higher education system in various aspects and promptly adjust the corresponding educational policies. Firstly, 23 higher education quality indicators with respect to more than 1,000 universities are carefully collected and converted to the corresponding indicators of 40 countries/regions. Moreover, such indicators are normalized by the range transformation method in order to facilitate subsequent analyses. Secondly, a novel fuzzy intelligence (F&I) method is proposed to model the health states of different higher education systems based on radial basis function (RBF) neural networks. This method firstly reduces the input indicators’ dimension by the principal component analysis (PCA) technique. Using the PCA results as the input layer, the RBF neural network is carefully trained and the F&I score of each country/region is therefore obtained. Next, the hierarchical cluster analysis is carried out to depict the health state of each county/region. 1, 5, 5 and 29 countries/regions are categorized as healthy, good, general and unhealthy, respectively.
Nitrogen purification pilot plant of Jiangmen Underground Neutrino Observatory

A nitrogen purification pilot plant has been developed to meet the requirements of high purity nitrogen used in the Jiangmen Underground Neutrino Observatory (JUNO). The radon content in nitrogen dropped from (20.5 ± 0.9) mBq/Nm3 (statistical error) to (25.2 ± 4.5) μBq/Nm3 (stat.) after purification. The Rn reduction factor of the nitrogen purification pilot plant was estimated to be 813 under the condition of 20 mBq/Nm3 of radon activity. Another experimental result showed that the radon activity dropped from (61.6 ± 7.5) μBq/Nm3 (stat.) to (19.2 ± 2.1) μBq/Nm3 (stat.) after nitrogen flew out from the nitrogen purification pilot plant. It shows that the radon activity of nitrogen after purified by the nitrogen purification pilot plant meets the cleanliness requirement of JUNO and the Rn reduction factor of the nitrogen purification pilot plant would decline when the radon activity of raw nitrogen declines to dozens of μBq/Nm3.
Recent advances in flexible sweat glucose biosensors

Continuous monitoring of the blood glucose levels is necessary for its diagnosis and treatment. In recent years, researchers have discovered a correlation between sweat glucose and blood glucose. The methodology of obtaining the value of blood glucose by detecting glucose in sweat is expected to replace conventional diabetes detection. Flexible sweat glucose biosensor owns the characteristics of stretchability, comfortability, and the capability of non-invasive and is a promising candidate for non-invasive diabetes monitoring. In this review, we summarize the latest developments in research and applications of the flexible sweat glucose biosensors based on enzyme and non-enzymatic detection methodologies. The correction methodologies, challenges and outlooks of the wearable sweat glucose biosensor technology are also be discussed.
Generalized Control Algorithm and Artificial Intelligence Technology for Quadruped Walking Robot

Quadruped walking robot has superior adaptability and high adaptive mobility in various environments. These special advantages are particularly prominent in the mobile robot community. In this work, we introduce the omnidirectional gait and the rotational gait approach, which is a generalized control algorithm for executing any directional instruction. In addition, in order to improve the stability of the quadruped walking robot, we optimize the walking Angle and foot order. The proposed ideas were applied to the practical design of MRWallspect III, which was designed to examine large surfaces in industrial facilities. The effectiveness of the proposed method is verified by experiments on a robot.
Application of Iterative Feedback Tuning Method in Superheated Steam Temperature Control System

Taking a 600 MW supercritical once-through boiler as the study object, this paper designed an IFT-based two-degree-of-freedom (DOF) PID superheated steam temperature control system. Besides, the IFT-PID controller was formed and compared with the conventional tuning method. The simulation results show that this method can be successfully applied to great-inertia and large-lag controlled objects, with evident superiority.
A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.
Visualization of Vortices Generated by Flapping Wing Device by Schlieren Method

Visualization and measurement of airflow detail structures is a difficult problem. In the past few years, high-speed DPIV has been used to capture the vortex structures on flapping robotic insect wings. Due to the inherent limitation in DPIV technique, important airflow activity in small scale vortical flow structures may be missed. In this paper, high-speed Schlieren photography was applied to vortical flow Visualization on a flapping robotic insect wings and then provided more information of vortical flow structures in detail. Based on the Schlieren image, quantified velocity vectors field was obtained by applying the variational optical flow method, which is able to be extending the potential applications to development flapping wings aircraft.
Discussion on the implementation of national water-saving actions in Changxing

Against the background of deepening Implementing the National Water-saving Action, Changxing adapts to local conditions by strengthening the dual control system of total water resources and consumption intensity. Changxing creating three major water-saving local characteristics such as building contract water-saving management system; promoting efficient and water-saving agriculture; promoting reuse of sewage water. Through these efforts, an upgraded version of water-saving in the new era will be built, comprehensively improve the level of water-saving in the whole society, also promote high-quality and green development of the economy and society.
A Text Category Detection and Information Extraction Algorithm with Deep Learning

In order to solve the problem that the text classification model based on neural network is easy to over-fit and ignore the key words in sentences in the training process, a Bi-GRU Chinese text classification model based on hierarchical Attention mechanism is proposed. The model introduces the idea of layering, uses bi-directional gated cyclic neural network to learn the text representation at word level and sentence level, uses Self-Attention hierarchical model to obtain the information of the influence of words and sentences on text classification, shares the weight between embedded layer and softmax layer by binding, and uses AMSBound optimization method to obtain the optimal weight matrix quickly and effectively while reducing the parameters in the model. Two commonly used Chinese data sets, FudanSet and THUCNews, are tested on the long Chinese text classification data set FudanSet. The experimental results show that the accuracy, recall rate and F-score of this model are better than Text-CNN model, Attention-BiLSTM model and Bi-GRU_CNN model, and the accuracy, recall rate and F-score index are improved by 5.9%, 5.8% and 4.6%, respectively.
An improved denoising model for convolutional neural network

In recent years, computer vision and many other research fields have put forward higher and higher requirements for image quality, and image denoising has become an important research direction. Aiming at the problems of general image denoising effect and long algorithm convergence time, this paper makes an improvement on the original network structure of denoising convolutional neural network DnCNN model and adds channel attention mechanism in the penultimate layer. The channel attention mechanism further improves the signal-to-noise ratio (SNR) and optimizes the noise reduction effect through convolution and deconvolution neural networks. The experimental results show that, under the same noise reduction level of 25, the PSNR of the proposed DnCNN2 algorithm is 1.25dB and 1.62dB higher than that of the DnCNN algorithm after the verification of the BSD68 and SET12 test data sets. Structural similarity (SSLM) was increased by 0.0011 and 0.0103, respectively. Meanwhile, the convergence time of the algorithm becomes shorter. Compared with traditional deep learning image denoising algorithms, the noise reduction effect and operation efficiency of the proposed method are also improved, which has certain advantages.
New Principle of Pilot Protection Based on Kendall’s Rank Correlation Coefficient

Affected by the control strategy after MMC failure, the fault current on the converter side of the AC line interconnected with the MMC is significantly different from the fault characteristics of the traditional power supply in amplitude and phase. The new fault current characteristics lead to the inapplicability of the traditional longitudinal current phase differential protection. In order to solve the problem of the applicability of relay protection, a new principle of longitudinal protection based on Kendall’s rank correlation coefficient is proposed. After the AC line fails, the Kendall grade correlation coefficient of the calculated current on both sides is calculated to distinguish the internal and external faults. Simulations are carried out on the RTDS platform, and the results show that the proposed protection principle can reliably distinguish between internal and external faults and has the ability to withstand transition resistance under various control reference values.
Fatal imported case of Plasmodium mixed infection: cerebral malaria, thrombocytopenia and multi organ dysfunctions (MODs)

*Correspondence chiara.angeletti@gmail.com (Chiara Angeletti) Abstract Background: Plasmodium falciparum accounts for nearly all severe malaria cases among European travellers, even though, in areas of endemicity, Plasmodium vivax seems to cause severe malaria to a degree which is comparable with P. falciparum. Furthermore, unlike countries at high risk, the incidence of mixed infections among imported cases is very low. We report the case of a man from non-endemic country with a mixed infection diagnosed after a two-year stay in Guinea. Case Presentation: A 43-year-old male Italian patient developed hyperpyrexia and chills the day after his return to Italy. Within 7 days from fever onset, he was found unresponsive with vomiting and seizures. On admission to Emergency Room, the patient was hypotonic with flaccid tetraparesis, with a GCS score of 7. On the basis of personal and working history and clinical features, after exclusion of viral encephalitis, acute pyogenic meningitis, tubercular meningitis and pontine infarct, cerebral malaria infection was suspected. Rapid diagnostic test and peripheral blood smear were positive for plasmodium mixed infection (P. falciparum, P. vivax, Plasmodium ovale). Over the first 30 hours from admission, thrombocyte count dropped, and the patient developed disseminated intravascular coagulation. Renal failure required renal replacement therapy. Death occurred after 48 hours of ICU admission. Conclusion: The fatal course of the severe malaria with related cerebral impairment, lung and kidney failure, disseminated intravascular coagulation, severe acidosis, circulatory collapse with refractory septic shock and subsequent multi organ failure was likely due to delayed medical presentation by the patient and the mixed plasmodium infection. Diagnosis could only be established nine days after onset of symptoms.
Impact of Krishi Vigyan Kendra on transferring knowledge to tribal farmers on improved animal husbandry practices

A study was conducted on the impact of KVK interventions on transferring knowledge to tribal farmers on improved animal husbandry practices in Panchmahal district of central Gujarat, to creating awareness regarding the efficient technologies through various efficient extension tools under the banner of ICAR- KVK, Panchmahal. A random sampling technique was applied to draw the samples of 120 farmers from eight villages of Kalol and Jambughoda Tehsil of Panchmahal district. Accordingly, strategies of technological intervention were made regularly during the period of study. In adopted villages, KVK, Panchmahal organized several activities like animal health camps, training programs, advisory services, and FLDs on different aspects of animal husbandry. The results of the study revealed that the means knowledge index and mean adoption index were recorded 39.24 and 93.81 and 77.24 and 25.43 percent, respectively for pre and after interventions. The impact of KVK interventions was found to be 53.19 percent over the existing knowledge and adoption by the trained farmers which were found to be substantial over the non-benefited tribal farmers on various aspects of animal husbandry after the intervention of Krishi Vigyan Kendra, Panchmahal. Based on the study it may be concluded that the overall improvement in the knowledge of the tribal farmers with respect to the adoption of animal husbandry technologies would be possible through the demonstration of efficient technologies needed for healthy livestock rearing which had not only created awareness but also improved the knowledge and attitude of tribal farmers in relation to scientific animal husbandry practices.
Exploration of Attention and Transformers for Question and Answering

The project was intended to be an exploration of convBERT model without pre-training, but after training a base BERT model (encoder only Transformer) and achieving very low performance, the objective shifted towards trying to understanding transformers and attention for Question Answering. Experiments on both hyperparameters and network architecture was done on the BERT model, with conclusion that this model will either overfit, or not converge. A hypothesis is suggested that without large corpus pretraining, simple self attention on a concatenated context and question has big difficiencies vs explicit cross attention to learn SQuAD. QAnet model was also trained for purposes of comparisons and analysis
Job Satisfaction among Nurses in Saudi Arabia: A Review of the Literature

The nursing shortage in Saudi Arabia continues to be significant to the point that over 60% of nurses in private and public hospitals in Saudi Arabia are foreigners. Identifying factors that impact nursing job satisfaction and dissatisfaction is critical to developing strategies to retain and recruit Saudi nurses. Although nurses in Saudi Arabia remain generally satisfied with the nature of nursing work, multiple factors may impact the level of satisfaction among nurses, affecting nursing recruitment and retention efforts. This information can be used to develop strategies for improving nurse satisfaction in the workplace. In addition, perhaps critical to these efforts is the effect of social views of nurses in Saudi Arabia and cultural factors that limit satisfaction, recruitment, and retention. The future of nursing in SA may hinge on a shift in cultural and social views of nursing. [Mohammed Alshmemri, Lina Shahwan-Akland Phillip Maude. Job Satisfaction among Nurses in Saudi Arabia: A Review of the Literature. Life Sci J 2016;13(9):78-83]. ISSN: 1097-8135 (Print) / ISSN: 2372-613X (Online). http://www.lifesciencesite. com. 8. doi:10.7537/marslsj130916.08.
Metaphors for spring in Vietmanese Song Lyrics A perspective from cognitive linguistics

This paper is conducted to investigates how Vietnamese people conceptualize the concept SPRING. In other words, the research aims at identifying both metaphorical expressions and conceptual metaphors of spring in Vietnamese. In this paper, attempts are made to describe conceptual metaphors from cognitive perspective based on theory of conceptual metaphors raised by Lakoff and Johnson (1980). The data consists of metaphorical expressions taken from 135 Vietnamese song lyrics in the 20 century onward. By employing description as the main method, and utilizing the procedure of conceptual metaphor identification (adopted in Steen, 1999: 73), the results of this paper shows that there are 15 conceptual metaphors of spring through concrete entities as source domains. It is hoped that this study could contribute to the area of metaphor research in cognitive perspective in Vietnam.
Effect of supplementation of multi-enzyme in low energy diet on the carcass traits and hematobiochemical parameters of broiler birds

The experiment was conducted to study the effect of multi-enzyme on carcass trait and hemato-biochemical parameters of broiler birds (Vencobb-430). A total number of 300 birds were reared for a period of 42 days with dietary treatments; T 0 -Control diet as per BIS (2007), T 1 - T 0 +multi-enzyme, T 2 - standard broiler diet with 2.5% reduction in energy than BIS, (2007) + multi-enzyme, T 3 - standard broiler diet with 5% reduction in energy than BIS, (2007) + multi-enzyme and T 4 -standard broiler diet with 7.5% reduction in energy than BIS, (2007) + multi-enzyme. Results showed that no significance difference for dressing, edible and giblet percent but the abdominal fat percentage was low in T 4 and breast meat percentage was ( P< 0.05) significantly higher in T 2 groups. There was no significant difference observed for the blood parameters. Blood glucose, Triglyceride and BUN was low in decreased energy treatment groups. It was concluded that multi-enzyme supplementation recommended in case of decreasing dietary ME level.
Different Routing protocols for Cognitive Radio Networks

: There has been rapid development in the evolution of various wireless devices and applications in recent eras. As a result, better utilisation of available resources and spectrum is needed. Cognitive radio networks have advanced, allowing for more efficient use of wireless spectrum. If the spectrum approach and selection is opportunistic in the spectrum band selection, it may be useful for unlicensed or primary users. The best available spectrum hole will be identified and used by the secondary user. In the recent years, many routing algorithms have been developed in this regard. This paper provides the different routing protocols for cognitive radio networks.
Nanocellulose production from lignocellulosic biomass in a Colombian context

Rosa Angélica Ospina Varón, Franz Edwin López Suárez, Valentina Aristizábal-Marulanda 1 Faculty of Natural Sciences and Engineering, Jorge Tadeo Lozano University, Bogotá, Colombia – rosaa.ospinav@utadeo.edu. co. 2 Engineering Department, Faculty of Natural Sciences and Engineering, Jorge Tadeo Lozano University, Bogotá, Colombia – franze.lopezs@utadeo.edu. co. 3 Faculty of Technologies, School of Chemical Technology, Chemical Process Development Group, Technological University of Pereira, Pereira, Colombia – valentina.aristizabal2@utp.edu. co.