Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Best Optimizer for Facial Expression Detection using Convolutional Neural Network

Deep learning is some sort of machine learning which resembles the features of the human's brain while analyzing data and generating patterns in order to draw conclusions. It is a part of machine learning, which in turns a part of artificial intelligence, and it consists of networks that are capable of learning from unsupervised, unstructured, or unlabeled data. When the size of the input data is vast, deep learning can outperform machine learning. Despite traditional programs, which develop insights with data in a linear fashion, deep learning systems include a hierarchical characteristic which permits machines to analyze the data in a non-linear fashion. Deep learning is advised for complicated tasks involving massive quantities of data, such as picture classification, natural language processing, and speech recognition In the scientific world, facial expression recognition is a passionately disputed issue. Human express their feelings using facial expressions. Humans can easily distinguish facial expressions, but computers find it challenging to do so because human facial expressions might fluctuate in photos due to elements such as brightness, contrast, and so on. This research uses seven main human emotions to train computers. Sadness, contempt, fear, happiness, anger, surprise, and neutral are among the emotions. Convolutional Neural Networks have the potential to solve among the most popular complex challenges in the fields such as deep learning and computer vision: facial emotion recognition. The system is being developed in three stages: image pre-processing, detection, and classification. Every iteration, the accuracy will be enhanced by fine-tuning key parameters for example, the initial thick layer's unit count, the rate of dropout in the dropout layer, as well as optimization strategies. Various optimizers, including as Adam, Adagrad, Stochastic Gradient Descent, and Nadam, were utilized to achieve the best accuracy.
Towards Cyber-Physical Special Protection Schemes: Design and Development of a Co-Simulation Testbed Leveraging SCEPTRE™

Unpredictable disturbances with dynamic trajectories such as extreme weather events and cyber attacks require adaptive, cyber-physical special protection schemes to mitigate cascading impact in the electric grid. A harmonized automatic relay mitigation of nefarious intentional events (HARMONIE) special protection scheme (SPS) is being developed to address that need. However, for evaluating the HARMONIE-SPS performance in classifying system disturbances and mitigating consequences, a cyber-physical testbed is required to further development and validate the methodology. In this paper, we present a design for a co-simulation testbed leveraging the SCEPTRE™ platform and the real-time digital simulator (RTDS). The integration of these two platforms is detailed, as well as the unique, specific needs for testing HARMONIE-SPS within the environment. Results are presented from tests involving a WSCC 9-bus system with different load shedding scenarios with varying cyber-physical impact.
Urban road checkpoints traffic flow prediction model of hyper-parameter based on PSO optimization LSTM

In view of the characteristics that traffic flow exhibits randomness, nonlinearity, and similarity due to the distinct environment, vehicles, roads, and people, this paper proposes an urban road checkpoints traffic flow prediction model of hyper-parameter based on PSO optimization LSTM. Which taking into account not only the influence of speed parameters, but also the traffic flow of upstream and downstream checkpoints on predicting traffic flow at target checkpoint, particle swarm optimization (PSO) is used to build a long short term memory model to optimize the learning rate, batch size, and the number of neurons of the model, the single long short term memory model (LSTM) is improved with the optimal hyper-parameter set. This paper takes the road checkpoints data in Xi'an as the research objective. According to the experimental results, the prediction accuracy of the model is far better than the traditional RF and SVM models, the error is significantly lower than the single LSTM model (working day: MSE 28%↓, MAPE8%↓; non-working days: MSE13%↓, MAPE3%↓).
Engineering of high-density network-shape muscle tissue using magnetic particles

The construction of skeletal muscle tissue has received much attention for clinical applications, such as muscle replacement and treatment of muscle-related diseases, but there are various problems with current types of tissue. In this study, we cultured mouse C2C12 myoblasts in reticular sheets to control cell orientation and examined the orientation of fibers. We generated muscle cells tissue with a strong local orientation and aligned the overall fiber direction.
Game Model in Multi-target Jamming Task Allocation

In electronic warfare, the electronic jamming is used to decrease the detect ability of airborne radar and the optimal allocation of jamming resources directly affects jamming effectiveness. In this paper, a task allocation strategy for multi-target jamming in dynamic air-raid combat is proposed by using the potential game theory. First, the multi-target jamming task allocation problem is formulated in dynamic scenarios. To cope with this issue, a jamming resource allocation game is proposed to find the satisfied strategy profile with limited jamming resource. In addition, we prove that the proposed game is an exact potential game and the Nash Equilibrium (NE) can be reached as the end stable solution. Finally, the best response learning algorithm is proposed for multi-target jamming task allocation. Some simulation experiments demonstrate that the proposed learning algorithm can converge to the optimal strategy of proposed game model with lower computation complexity.
LDA-LPA: A Community Detection Method Based on Topic Model

Community detection in complex networks is an important task in network analysis. And community structures in networks can be detected from many aspects. However, community detection based on topic model is rarely studied, and most of the existing community detection methods based on topic model are generally based on node or edge attributes, such as text information. In this paper, a novel community detection method based on topic model is proposed, which only requires the structural information of networks, and does not need node or edge attributes to be presented. In addition, this paper also proposes a new graph embedding method based on the neighborhoods of node, which is simpler and does not need the complicated training process. Experimental results show that the community detection method proposed in this paper is superior to most benchmark algorithms for community detection. In particular, on some datasets, the method proposed in this paper can achieve the division of normalized mutual information of 1.0, that is, completely correct community division results can be obtained.
Reactive Power and Voltage Control Issues Associated with Large Penetration of Distributed Energy Resources in Power Systems

As the penetration of distributed energy resources increases and large conventional generators are retired, voltage regulation of generators and reactive power control of the power system should be considered more carefully. In this paper, the impacts of adding renewable resources and changing generators’ voltage regulating buses on static power flow convergence and voltage profiles are studied and reactive power status of critical areas as well as online and offline reactive power reserve capacities are visualized. The results show that if renewable resources are added without adding sufficient reactive power resources or if the voltage regulating bus of all generators move to the low voltage side of step-up transformers without tuning the voltage set points of generations, voltage violations and even voltage collapse may occur, which have a negative impact on the reliability of the power system. The paper also reviews possible solutions such as tuning the voltage set points of generators for reactive power management and adding voltage control devices such as switched shunts or static var compensators (SVCs) to the grid to compensate for this change.
Eco-driving speed optimization model of urban intelligent connected vehicle platoon considering driver's comfort level

In order to solve the problem of low driver's comfort level and high fuel consumption caused by the complex traffic flow characteristics on urban traffic arterial roads, this paper proposes an eco-speed optimization model of intelligent connected platoons considering the driver's comfort level and fuel economy. The model is based on the red light phase, taking into account the safety distance constraints of vehicles, and using genetic algorithms to optimize the solution to obtain the optimal vehicle speed curve, to guide platoons smoothly pass through the urban traffic arterial roads. In order to verify the performance of the proposed model, the simulation test of traffic scenarios with and without eco-driving speed optimization model is carried out in this paper. The test results show that the speed optimization model proposed in this paper can reduce vehicle acceleration by 21.5%, improve the comfort of drivers and passengers to a certain extent, and reduce fuel consumption by 31.6%. Therefore, the model can adjust the speed of vehicles by forming platoons on urban traffic arteries, thereby effectively improving fuel economy and improving the comfort of drivers and passengers.
Damage imaging in composite plate by using double-turn coil omnidirectional shear-horizontal wave magnetostrictive patch transducer array

A double-turn coil omnidirectional shear-horizontal wave magnetostrictive patch transducer (DC-OSH-MPT) array for imaging detection in composite plates is presented. DC-OSH-MPT parameters are optimized in order to make the transducer more suitable for imaging detection. Besides, the characteristic of the lowest shear-horizontal mode (SH0), which can be generated and received in all directions by DC-OSH-MPT bonded on composite plate, is beneficial for imaging. Due to anisotropic property of composite plates, the group velocity of SH0 mode in different azimuthal directions was experimentally measured in damage imaging. The location of simulated damage, which was a bonded circular cylinder, was estimated by DC-OSH-MPT array and ellipse imaging algorithm. The imaging results have demonstrated that DC-OSH-MPT can be considered as a spatially distributed array suitable for imaging detection in composite plates.
IMPROVEMENT OF ESOPHAGEAL VOICES' PITCH

In this paper it is described a new algorithm for esophageal speech regeneration, based on pitch and jitter modification. Traditional phase vocoder and resampling pitch scaling techniques have been used to develop a new adaptive method which scales the low esophageal speech pitch and applies a variable scaling factor significantly reducing its jitter. This method has shown to considerably improve esophageal speech quality, reducing its hoarseness and increasing its intelligibility. The presented algorithm pretends to be an important step forward in the regeneration of esophageal speech. Laryngectomees have to generate their speech by insufflation of the esophagus and vibration of the pharyngoesophageal segment in replacement of the vocal folds. These special conditions make esophageal speech’s feature parameter differ from normal speech. This algorithm is part of a set of algorithms intended to correct these parameters. Among esophageal speech irregularities there are many related to the pitch. The fundamental frequency of the resulting speech signal is lower than the pitch of normal voices, due to the different shape and size of the vibration organs. However, this is not the only problem. The unsustained air flow responsible for the vibration of the pharyngoesophageal segment causes the decrease of the fundamental frequency which does not remain constant over time. Therefore, esophageal speech has a very low pitch and a very high jitter [1]. With the aim of correcting these irregularities, the designed algorithm raises the fundamental frequency and lowers the jitter of esophageal speech. In this aspect the algorithm constitutes a total revolution for the esophageal speech treatment, and sets up many new lines of investigation.
Ostra: uma abordagem para análise da qualidade de software por meio de regras de associação de métricas

A key element to control the software quality, not only reacting to its variation, is the understanding of which factors influence the quality attributes and how they influence each other. In this work, we present the Ostra approach, which allows the historical analysis of software by mining association rules of its metrics, extracted from the historic information stored in version control systems. The Ostra ’s goal is to provide information to decision-making process. We also present experiments in which the Ostra approach is applied to real projects. With these experiments, it was possible to find evidences that the proposed approach can achieve its goals.
Optimized LEACH protocol based on cluster-head option and number of nodes

To improve the properties of network as a whole and save the energy,the weighting factor is introduced to the LEACH protocol to optimize the cluster head based on the residual energy of every node,average residual energy of the network and the average consumption of former circle.The optimized numbers of nodes are determined according to the energy consumed during the data transmission,energy consumption of cluster head nodes and ADV transformation energy consumption.Matlab simulation results demonstrate that there are such features such as low energy consumption,even node distribution and long-life span in the improved protocol,compared with other protocols.
Analysis by STAAD-PRO and Design of Structural Elements by MATLAB

The analysis and design of multi storey building is carried out usually in the software packages which are very strong in analysis. The different software like STAAD-PRO, Etabs, etc is used for such purposes. The programming tools like C+, JAVA, etc are also used in many structural elements for different methods of calculations. The high level programming tools like MATLAB is used for many complicated problems in various fields. In the present paper the STAAD-PRO is used for the purpose of analysis and design of a building. The building was analyzed for the seismic behavior. Shear force, bending moment, deflections are calculated using the software, the reinforcement details are also available through the design. The coding of STAAD editor is also included in this paper. The design of slab, beam, column and footing are carried out by the programming of MATLAB. The objective of this study was to check the programming language for these structural elements.
An Investigation of Fast Tool Servo Machining of Optical Microstructures

Optical microstructures are small-scale topologies generally classified as grooves, pyramids, and micro-lens arrays which are finding applications in flat-panel displays used in hand-held devices such as mobile phones. As more stringent requirements are being imposed on the miniaturization of telecommunication products, the conventional etching process is found to be inadequate to fabricate high quality optical microstructures. This paper presents an effective technology for the fabrication of high quality optical microstructures based on fast tool servo ( FTS) machining system incorporated with a purposely built tool path generator (TPG) . The TPG allows the generation of a tool path directly from the design of the optical microstructures without the need for any data post-processing. The capability of the FTS machining system is demonstrated through a case study and satisfactory results were obtained.
Predictive significance of inflammatory indexes in metastatic nonsmall cell lung cancer patients treated with platinum-doublet chemotherapy

Introduction: Lung cancer is the leading cause of cancer-related deaths, worldwide. Despite immune checkpoint inhibitors and targeted therapy have revolutionized the treatment of metastatic nonsmall cell lung cancer (NSCLC), a significant proportion of patients are still treated with platinum-based chemotherapy. It is widely noticed that systemic inflammation plays an important role in the development and progression of many solid tumors. In this study, we aimed to analyze the predictive role of the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index (PNI) in patients with advanced NSCLC treated with first-line platinum-doublet chemotherapy. Methods: We retrospectively collected data of patients treated with first-line therapy for metastatic NSCLC. All patients were treated with first-line platinum-doublet chemotherapy. The patients were grouped based on the median values of SII, PNI, and NLR. The Mann–Whitney U test was used for comparisons between groups. Results: The chemotherapy response rate (RR) was 75% in all patients. RR is statistically significantly lower in high SII, low PNI, and high NLR groups. Conclusions: High inflammatory indexes in metastatic NSCLC patients who were treated with platinum-doublet chemotherapy are related to low chemotherapy RR.
Sum-rate optimal network beamforming and power allocation for single-carrier asynchronous bidirectional relay network

Sum-Rate Optimal Network Beamforming and Power Allocation for Single-Carrier Asynchronous Relay Network Mina Askari Master of Applied Sciences Graduate Department of Electrical and Computer Engineering University of Ontario Institute of Technology August 2015 We study the problem of sum-rate maximization, under a total transmit power budget, for an asynchronous single-carrier bidirectional (two-way) network. The network consists of two single-antenna transceivers which wish to exchange information with the help of multiple single-antenna amplify-and-forward (AF) relays. We assume that the network is asynchronous meaning that different transceiver-relay links cause significantly different propagation delays in the signal they convey. As a result, the end-to-end channel is not amenable to a frequency flat model, rather a multi-path channel model with multiple taps appears to be more appropriate. Such a multi-path model for the end-to-end channel raises the issue of inter-symbol-interference (ISI) at the two transceivers. In a block transmission/reception scheme, ISI leads to inter-block interference (IBI), which could result in loss in the sum-rate of the network, if it is not considered in the design of the system. Considering a block transmission/reception scheme and assuming a total transmit power budget, we maximize the sum-rate of this ISI end-to-end channel over the relay complex weights and transceivers’ transmit powers. We rigorously prove that such a sum-rate maximization problem leads to a relay selection scheme, where only those relays which contribute to one tap of the end-to-end channel impulse response are turned on and the rest of the relays are switched off. Indeed, we prove that at the optimum, the end-to-end channel impulse response (CIR) has only one non-zero tap, rendering the end-to-end channel frequency flat. We present the optimal value of the vector of the weights of the active relays and the optimal values of the transceivers’ transmit powers in a semi-closed form.
Robust H_∞ Estimation of Airspeed Based on Acoustic Vector Sensor Array

The measurement of airspeed based on an acoustic vector sensor array is studied in this paper.According to the propagation principle of acoustic waves in an air current,the relationships between particle velocity and air current velocity are first derived and a measurement model based on a linear acoustic vector sensor array is built.Then a novel approach for estimating airspeeds based on the robust H∞ filter is proposed.By the phase lag between each sensor,the proposed algorithm is implemented as an iteration to estimate the airspeed.The selection of system initial parameters is discussed and the robustness against system perturbation is analyzed in theory.Finally,simulation results show that the proposed approach has excellent robustness and toleration of sensor failures.
Integrating the Orca Optimizer into MySQL

The MySQL query optimizer was designed for relatively simple, OLTP-type queries; for more complex queries its limitations quickly become apparent. Join order optimization, for example, considers only left-deep plans, and selects the join order using a greedy algorithm. Instead of continuing to patch the MySQL optimizer, why not delegate optimization of more complex queries to another more capable optimizer? This paper reports on our experience with integrating the Orca optimizer into MySQL. Orca is an extensible open-source query optimizer—originally used by Pivotal’s Greenplum DBMS—specifically designed for demanding analytical workloads. Queries submitted to MySQL are routed to Orca for optimization, and the resulting plans are returned to MySQL for execution. Metadata and statistical information needed during optimization is retrieved from MySQL’s data dictionary. Experimental results show substantial performance gains. On the TPC-DS benchmark, Orca’s plans were over 10X faster on 10 of the 99 queries, and over 100X faster on 3 queries.
Survey Paper On Different Types Of Techniques used for Credit Card Fraud Detection

Credit Card fraud detection is considered as one of the major challenge that the digital world witnessed today. During this pandemic situation the use of credit card transactions has been increased tremendously. Hence fraud transactions also . The credit card fraudulent becomes a major security threat faced by the financial industry. Many modern techniques like genetic algorithm, machine learning, data mining, fuzzy logic etc has been used vividly used for detecting such fraud transactions. A clear analyzing and sound understanding about all these techniques will give an efficient credit card fraud detection system. This paper aims to presents survey of various techniques used in credit card fraud detection and evaluates each approach based on defined design criteria .
Intelligent Data Analysis Using Neuro-Fuzzy Approach

: This paper examines the use of neuro-fuzzy techniques to perform intelligent data analysis. The advantages and disadvantages of using neural networks and fuzzy models in intelligent data analysis are investigated and reported. A comparison is made of neural networks, fuzzy rules extracted from data and fuzzy rules extracted from trained neural networks. A neuro-fuzzy data analysis model is also proposed. It is often observed that the data analysis cycle consists of three main steps: feature selection, model construction and interpretation of the model. It is shown that the use of the proposed neuro-fuzzy model can be beneficial in all the three steps. The proposed technique generates results as good as that of other techniques and possesses desirable features when compared to other techniques in the literature.