Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics and control of entrained solutions in multi-mode MooreGreitzer compressor models

In this paper we investigate the behaviour of higher order Galerkin expansions of the Moore-Greitzer model of general transients in aeroengine compression systems. We assume steady state entrainment of the higher Fourier modes of the rotating stall cell which helps establish a framework for a simplified numerical analysis of the bifurcating solutions corresponding to rotating stall. For small values of the Greitzer surge parameter (B) we discuss general trends in the character of the pure stall solutions. The rotating stall characteristic is shown to exhibit deep hysteresis with a cubic compressor characteristic, establishing the fact that deep hysteresis to a certain extent is a multi-mode phenomena. Elimination of the hysteresis associated with the bifurcation into stall is accomplished in simulations with a combined feedback on the displacement from the peak of the compressor characteristic and the magnitude of the first mode amplitude of the stall cell. Behaviour for larger values of the B parameter i.
Ceramic Decoration Extraction Method Based on Computer Vision and Image Processing

China is one of the first countries to invent pottery in the world. Like India, West Asia, Japan and the central Balkans, our ancestors invented pottery in the Neolithic period of primitive society. As a product of computer technology, the development and application of graphics processing technology has been extended to all sectors of society, especially in the field of ceramic decorative pattern design. The speed and accuracy of traditional intelligent manufacturing detection can not meet the current detection requirements. Machine vision detection technology captures the image through the vision system, uses the Prewitt directional derivative approximation operator to detect the edge of the copied image, and completes the template matching and matching value weighting by transforming the image detection space into the parameter space. After non-maximum suppression, all directions of corner response function are processed, and the right corner output and its direction are obtained. After expanding the corner area, the detection area is defined. Zernike moment discretization is used to process the real part and imaginary part of the image, and the image features are extracted. Combining the coordinates of each corner and sub-pixel coordinates of the image, the fake area and the real area are determined, so as to realize the image tampering detection. In this paper, we focus on the body patterns of ceramic artworks, aiming at high-quality reproduction and design re-creation, deeply study the collection and image correction technology of three-dimensional patterns of ceramic artworks, and deeply analyze the algorithm of feature detection and extraction.
Research of the Radiation Characteristics of the Vivaldi Radiator Based on Various Laws of the Expanding Slot

The implementation of wireless communication devices requires the development of multiband and broadband antennas with improved characteristics. One of the directions of development is tapered slot antennas. This paper presents the results of a study of antipodal Vivaldi antennas. Their designs differ in the way the aperture is constructed. Linear, exponential and Fermi aperture were implemented, and their modifications with the addition of sinusoidal corrugation were presented. The range of the investigated frequencies is 1-20 GHz.
Joint Extraction of Entity Relations Based on Dependency Syntax and BERT in Medical Domain

The traditional Pipeline algorithm has been widely used for relation extraction tasks due to its advantages of easy implementation. More recently, Joint-based methods in the general domain have solved the problems of error accumulation and entity redundancy in Pipeline and have continuously achieved excellent performance. However, for vertical domains (this study focuses on the medical domain), there is still room for continuous improvement of existing Joint methods. In this research, we propose to use dependency syntax and pre-trained models in the medical domain to obtain entity relationships in a parameters-sharing approach. Finally, the improved method was validated on the general-purpose dataset NYT and the medical dataset NCBI-disease. Selecting Normals data for the experiments, the algorithm improved the F1 values by 5 percent and 2.9 percent on the two datasets, respectively. For the SEO data, the F1 values were improved by 1.5 percent and 1.4 percent.
Usability evaluation from client-side traces using heatmap and system usability scale

In this project, we investigate the usability of a gamified application developed as part of the e-learning system for the University of Souk Ahras. The online application is integrated within the university web portal, allowing students and lecturers to ask questions and receive answers from other students or lecturers within the university about any modules. This would result in creating a knowledge base of questions and answers for every module that future students could benefit from without the need to ask their lecturers again. We propose the use of heat maps for assessing usability based on client-side traces where a JavaScript app is used to track user behaviors while exploring the university website remotely without accessing or modifying the main university website. The suggested tool is integrated into the webpage by inserting a single line of JavaScript code. After logging and storing the data, a heat map is generated from this data using PHP and JavaScript to assess the most active areas where users payattention when interacting with the website. Furthermore, we created an online implementation of the System Usability Scale questionnaire in order to assess the satisfaction of university participants in using the Question Board platform. The SUS is considered after using the platform. For users who couldn't manage to use the application, we have devised a special questionnaire. Finally, a questionnaire is devised for users who haven't used the application in order to figure out the main reasons for the weak turnout on the platform
Dynamic Semantic Modeling of Structural Data Sources

A semantic model of a data source is a represen-tation of the entities and relationships contained in the data. Building semantic models is a prerequisite for automatically publishing data to a knowledge graph. However, constructing these semantic models is a complex process requiring consid-erable manual effort and a high potential for errors. Several approaches have been proposed for automatically identifying semantic models of data sources. However, as the schemata of data sources change, the corresponding semantic models may require amendments. We propose a dynamic semantic modeling approach whereby the semantic model of a data source can be adapted automatically as data sources change. We show that our incremental approach outperforms state-of-the-art systems by an average of 7.4% in the F1 score and that it is orders of magnitude faster than constructing a semantic model from scratch.
Comparative Analysis of Machine Learning Approaches in Smart Agriculture

In India's economy, agriculture is crucially significant. Agriculture automation is a major source of concern and a hot topic all around the world. The world's population is constantly growing, and with it comes increased demand for food as well as occupation. The farmers' old methods were inadequate to satisfy these requirements. As a result, new automated approaches were proposed. These new methods met food demands while simultaneously providing work opportunities for billions of people. Several methods are used for efficient farm management like IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Among these big data is an emerging research area for crop yield prediction. Traditionally, these forecasts were reliant on the farmers' expertise, but now they can roughly anticipate the crop output on their farm by employing a variety of methods. In this paper, we have proposed a recommendation model along with an algorithm to evaluate the future year's crop production. We have compared the random forest machine learning algorithm with the big data algorithm. A brief comparison has been made with these algorithms. The proposed model has been implemented using python.
Hybrid Decision Based Multi-Agent Deep Reinforcement learning for Task Offloading in Collaborative Edge-Cloud Computing

To support computing-intensive and latency-sensitive Internet-of-Things (IoT) applications, we establish a three-layer collaborative edge-cloud computing network, in which multiple IoT devices, edge servers and cloud servers are allocated at IoT layer, edge layer and cloud layer, respectively.However, it is still challenging to satisfy the service requests with inevitable resource competition among task offloading. To solve the joint computation offloading and resource allocation problem, we formulate it as a multi-agent Markov decision process (MAMDP), where action space of each agent contains discrete-continuous hybrid decision. Firstly, we relax the discrete action to a continuous set by developing a probabilistic method. Then, by taking advantage of a continuous action based centralized training and distributed execution strategy, a cooperative multi-agent deep reinforcement learning (MADRL) based framework with each IoT device acts as an agent is established. The objective function is to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of edge/cloud servers. The simulation results demonstrate the advantageous of the proposed MADRL over the three state-of-the-art DRL based agents in reducing the system cost.
Surface Recognition from Wheelchair-induced Noisy Vibration Data: A Tale of Many Cities

Despite the active legislation in many countries supporting the accessibility of public spaces by mobility-impaired users, the reality is far from ideal. Wheelchair users often struggle to navigate the built environment let alone the natural areas. While barriers to wheeled mobility can be caused by broken/uneven surfaces, steep slopes, and unfavorable weather conditions, the effects of many such factors and others are not properly investigated. In this paper, we aim to classify various built and natural surfaces through their characteristic vibration patterns using different deep learning algorithms. The surface vibration data is collected from various cities in Europe (including Paris (FR), Mannheim (DE), Dresden (DE), Munich, Nuremberg (DE), and Salzburg (AT)) while a user drives a manual wheelchair attached with three differently oriented smartphones placed at different heights. Extensive experiments show that a Deep Neural Network model classifies surfaces using a denoised dataset with a 98.9% accuracy which is significantly higher than our previous state-of-the-art.
Hybrid model for detection of frauds in credit cards*

The goal of this study is to decide on the correct model that can predict a credit card holder’s likelihood of missing their next payment based on their demographic characteristics and payment history. Our goal is to reduce the number of defaulters that are labelled as non-defaulters. That’s because our company can afford to have non-defaulters who do default but not non-defaulters who do not default, as it is implied that the bank has a stringent no-NPA policy (more or less). When putting machine learning algorithms into practice, supervised learning must be used since we have known upstream and downstream values and anticipate a prediction model at the conclusion. The correlation amid attributes and target sets is established in the first stage. Classification stage classifies input data into certain categories. In order to examine the performance of recommended approach, the comparison of recommended approach and other available approaches has been conducted in this work along with this achieving the 85% accuracy result.By accurately categorizing credit card delinquencies and non-defaulters, the mortgage lenders will be able to utilize the accuracy of the model to lower the high delinquent rate.
Reliability Analysis and Optimization of Computer Communication Network Based on Policy Optimization

With the popularization of computers and networks in the development of various industries at this stage, the network connection area and scale are also expanding, and its reliability has gradually become the focus of social attention. For computer networks, reliability is a key issue. We should focus on the macro level, conduct systematic analysis and research, and take effective measures to optimize computer networks and improve their reliability and security. This paper first expounds the characteristics and design principles of computer communication network (CCN) reliability. Secondly, the relevant theory of CCN reliability is introduced, many factors affecting the reliability of CCN are analyzed, and the bottleneck problem that restricts the improvement of CCN reliability is pointed out.
Learning Management System: An Approach towards Virtual Education

With India’s rapid educational growth towards virtual education, the education system has intensified. India has gone through critical condition like COVID-19. There is huge increment in the implementation of online learning management system. This helped the educational institutions to manage their education system without being physically present in the institution. This paper is presenting the challenges required by institutions to transform their manual education system into virtual/online education system. The online learning management system has enhanced the way of teaching and learning process of students. This study will tell us about how online LMS has speeding up the quality of education for the On-Campus enrolled students as well as distance learning students also. In general, LMS are built to provide the course material in a static way only. The modern LMS has the ability for the group discussion which is helping the student to achieve the qualities of team-working.
An Improved Object Detection Method Based on NAO Robot

As a popular humanoid robot in recent years, NAO robot has been loved by the majority of researchers because of its rich functions and compact characteristics. When NAO robot is applied to face recognition, tracking, grasping and other scenes, whether it can effectively detect and recognize the object will directly determine the deployment of subsequent tasks. At present, the object detection algorithm used by NAO robot is relatively simple, which is greatly affected by the environment and light. At the same time, the categories that can be detected are relatively single. In order to solve the above problems, this paper introduced the current mainstream one-stage detector (YOLOv5) into the object detection system of NAO robot, and designd a set of object detection algorithm deployment process for the deployment environment of NAO robot, which effectively improved the object detection capability of NAO robot.
An Improved P&O Method MPPT Control Strategy Based on Model Predictive Control for PV System

Aiming at the problems of the traditional P&O method can't guarantee the dynamic performance and steady-state accuracy at the same time when the external environment changes, for PV system, an improved P&O method maximum power point tracking (MPPT) control strategy based on model predictive control is proposed in this paper. Firstly, the motion of PV operating point is divided into forward motion and reverse motion, and based on this, the perturbing step is divided into large and small step sizes. The forward motion corresponds to small step and the reverse motion corresponds to large step, so as to reduce the oscillation of the PV at the maximum power point (MPP) and increase the dynamic performance and robustness of the PV system for external environmental changes. Secondly, by analyzing, it can be known that the impact of solar irradiance on PV output power is much greater than that on PV output voltage and current. Therefore, the changing value of PV output power is used to judge whether solar irradiance change occurs, and if there is a change for the solar irradiance, the reference current will be modified, further more to improve the dynamic performance and robustness of the PV system. Finally, the method proposed in this paper is compared with the conventional P&O control with fixed, variable and adaptive step sizes to verify the feasibility and effectiveness of it.
Generating Higher Order Mutants using PSO with Levy Flight(LFPSO) Algorithm

Nowadays, test data generation via a search based become popular, and mutation testing plays a very essential role by improving the efficiency of test data creation. In this research, LFPSO with Mutation Testing, a high-order mutation test data generating method, was presented, which was then implemented on large programs from the Software-artifact Infrastructure Repository (SIR). The suggested algorithm LFPSO is then compared against GA, as well as the most extensively used method for developing test data, CRO. For the majority of the benchmark programs, the results show that LFPSO outperforms GA and CRO in terms of computing efficiency.
In chicks wearing high powered negative lenses, spherical refraction is compensated and oblique astigmatism is induced

Chicks develop refractive errors in response to defocusing lenses. Mean ocular refractions (MOR) and astigmatism were measured in chicks that developed myopia in response to unilaterally applied negative lenses (-30 D, n = 19; -15 D, n = 16). The MOR of the control eyes emmetropized with age and both goggle powers were fully compensated given enough time. The initial slopes of plots of MOR with age are not significantly different between inducing lens powers. Astigmatism in control eyes decreased significantly with age. In the presence of -30 D lenses, astigmatism significantly increased with age due to an exponential increase in JCC45 (oblique Jackson crossed cylinder). Although not statistically significant, a similar but smaller trend was observed for the -15 D lenses. Thus, oblique astigmatism results during myopia induction by high-powered lenses. This induced astigmatism is correlated with MOR. These results are consistent with emmetropization of astigmatism, which is disrupted by high-powered spherical lenses.
Controllability of Continuous-Time Bilinear Systems Without Using the Lie-Algebraic Conditions: A New Perspective

Lie algebra methods are powerful for studying controllability of continuous-time nonlinear systems and most controllability criteria of continuous-time nonlinear systems are Lie-algebraic conditions. However, the Lie-algebraic conditions are in general difficult to verify if the system dimension is high. In this article, we propose a new perspective to study controllability of continuous-time bilinear systems without using the Lie-algebraic conditions. Specifically, we first consider controllability of the bilinear systems in the single-input case under a commutativity condition. We show that, although the Lie algebra rank condition for the homogeneous counterparts, which is necessary for a classical controllability result to work, does not fit such bilinear systems, they can still be controllable. Our approach to proving controllability is using controllability of the discrete-time counterparts of the continuous-time systems, and we derive a necessary and sufficient controllability criterion without the Lie algebra rank condition, which is algebraically verifiable for any finite dimension. More importantly, through this controllability study, we propose a new perspective to deal with the controllability problems of continuous-time bilinear systems by changing the verification of the Lie algebra rank condition to solving two linear algebra problems. That is, we establish a new method beyond Lie algebra methods to controllability of continuous-time bilinear systems. We also generalize the proposed perspective to continuous-time state-affine nonlinear systems to obtain algebraic controllability criteria. Examples are given to illustrate the obtained controllability results of this article.
Research on Load Optimization of Software-Defined Network Based on Delay and Load

Software-defined network (SDN) is a new network paradigm in recent years. Because of its advantages of global view and network programmability, SDN-based data center load balancing has gradually become a research hotspot in computer domain. Traditional load balancing methods can't effectively obtain the statistical data of network equipment or the factors influencing load balancing are too single, which leads to low efficiency and poor flexibility. Because of its specific network structure, the software definition network has the characteristics of centralized control over the acquisition and distribution of global network resources. The method adopted in this paper is ant colony algorithm, which randomly selects the value in a certain range according to the value setting of the route pheromone, so as to ensure that the ants do not rely solely on pheromones when choosing the route, but have certain “innovation” to prevent the algorithm from falling into local optimum. When the ants have traversed all the nodes, in order to avoid the left pheromones from volatilizing and make them more effective, it is necessary to update the left pheromones in time. The research shows that this algorithm is superior to the traditional algorithm and can be widely used.
Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface

Various convolutional neural network (CNN) -based models have been proposed to improve classification performance in the MI (motor imagery) -based BCI (brain-computer interface) dataset with multiple subjects. However, most studies have not investigated whether the subject-transfer with fine-tuning is effective. In this study, we proposed a subject-transfer method with subject-specific fine-tuning based on Multi-Model CNN and compared classification accuracies with various CNN models. For evaluation, we used the public 2020 international BCI competition track 4 datasets with 15 subjects and 2 sessions. Each CNN model was pre-trained with other subjects’ training sets, fine-tuned with the target subject’s training set, and evaluated on their validation set. The classification accuracy of the proposed subject-transfer method with Multi-Model CNN (59.44± 16.57%) was significantly increased compared to the conventional method (57.37± 15.92%). The proposed subject-transfer method can contribute to developing effective models with high classification accuracy in datasets with multiple subjects and sessions.
Real-Time Driver Drowsiness Detection Using Dlib And openCV

Driver fatigue has been an important factor for large number of vehicle accidents. Drowsy-driving crashes are estimated to result in roughly 800 deaths and 50,000 injuries each year, according to the National Highway Traffic Safety Administration. In the field of accident prevention systems, developing solutions to detect or prevent drowsiness is a serious challenge. Because in case of drowsiness, there is a need to develop methods to counter its effects, presented on the road. The driver's eyes will be monitored in real time to see if they are open or closed. It is thought that indicators of driver weariness can be recognised by monitoring the eyes in order to avoid an automobile accident. Images of a face, as well as observation of eye movements and eyelid patterns, are part of a fatigue detection sequence. The landmarks of frontal driver facial in a frame are obtained after the position of the eyes is determined using the Dlib toolkit. A new parameter, termed Eyes Aspect Ratio, is included to measure the tiredness of the driver in the current frame based on the eyes, landmarks. The study's findings can be used to improve smart transportation systems, ensure driver safety, and eliminate drowsy driving-related losses.