Miracle-dz/UniFAR-FTUs huggingface dataset:
A/B Testing for Better Instruction of Crowdsourcing using Real and Virtual Workers

In this paper, we propose a method for selecting adequate additional instruction for workers in microtask crowdsourcing. In this context, “adequate instructions” means that workers will appropriately do the task if the workers read and understand the instructions. Instructions for workers should be easy to read and understand, but the requesters are hard to recognize whether the instructions are adequate or not. Therefore, we propose a method for measuring the ade-quateness of instructions using machine learning. We assume a labeling task for tweets. In our method, we construct the machine-learning model for each worker, which labels similar to the worker, which we call a “Virtual Worker. ” Then, the machine-learning model labels do the tasks for all tweets. Finally, we calculate the kappa-values of this labeling. We assume that if an instruction is inadequate, the kappa values should be high. In our experiments, we found that our method can select adequate additional instruction.
Lane Keeping and Navigation of a Self-driving RC Car Based on Image Semantic Segmentation and GPS Fusion

This work aims to develop a lane-keeping and GPS navigation system for a self-driving vehicle on a real road. For the lane-keeping task, we trained and deployed a light-weight bilateral semantic segmentation network to segment out drivable area on the road from the camera along with other objects like cars and humans and derived the steering formula for the vehicle. As for navigation, we use a GPS receiver and an IMU module to estimate the position, heading, and orientation of the vehicle with respect to a designated GPS track. To improve the result of state estimation, we modified the Extended Kalman Filter to effectively estimate the state of the vehicle by fusing GPS and IMU sensors. In the end, we discuss a fusion strategy to navigate the vehicle in different road scenarios. The system runs on an Nvidia Jetson TX2 on a 1/10 RC car model, the experiment and measurements were conducted on the internal road of HCMCUTE campus.
FTnet-based Digital Demodulator for Radio over Fiber Transmission

A digital demodulator based on Fourier layer Transformer network (FTnet) is proposed for radio over fiber (RoF) transmission with intensity-modulation and direct-detection (IM-DD). The FTnet is a new deep learning network, which replaces the self-attention mechanism of the Transformer encoder with a Fourier layer. The FTnet-based digital demodulator can directly recover corresponding bitstreams from the impaired receiving signal, which replaces a series of digital signal processing in the RoF traditional digital demodulator, including down-conversion, matched filter, downsampling, phase offset compensation, equalization, decoding, etc. A 10 GHz 16/64QAM 2 Gsym/s 25 km RoF transmission system is established to verify the proposed method. The experimental results show that the receiving sensitivity for 16QAM and 64QAM systems with the proposed demodulator can be improved by 1 dB and 5 dB respectively compared to traditional digital demodulator with LMS equalizer under a bit error rate limit of 3.8×10−3.
Leaderless Consensus of Lipschitz Nonlinear MASs via Integrated Adaptive Control

This paper proposes an integrated adaptive control protocol for the leaderless consensus problem of Lipschitz non-linear multi-agent systems (MASs). Such an integrated protocol breaks the limitation of the existing adaptive protocols requiring all edges to have a common feedback control gain in the column space of the input matrix. By constructing the Lyapunov function partly by the trace-norm of the adaptive coupling gain matrix, it is proved that the consensus can be achieved asymptotically under the proposed control protocol. A numerical example on Chua's circuits is given to illustrate the effectiveness of the integrated protocol.
Automating the Process of Developing Obfuscated Variants of PE Through ADOPE Software

Due to the widespread use of obfuscated malwares and malicious PEs, the anti-virus systems are outdated to new attacks. Since most anti-virus software doesn’t provide 100% detection, this puts the user’s data and privacy in jeopardy. ADOPE is developed to train AI based models for dealing with obfuscated versions of malware. The phenomenon of obfuscation is adapted to make the malicious sample difficult to analyse which leaves all anti-virus systems incapable of detecting the maliciousness in sample. The tool will automate the production of obfuscated variants in PEs by using different techniques of obfuscation that are used by malware analysts to generate obfuscated datasets and train machine-learning models to enhance their predictions.
Time Analysis for Sharing on Twitter Social Network by Using Neural Networks

With the increasing popularity of social networks in recent years, millions of users interact with other users daily. Individual users and corporate useAs a result ofrs are very active on social networks. Almost all brands have social network accounts and share advertisements and announcements about their brand on these accounts. Interaction on shares of the brands in social networks plays a significant role in increasing the brand's awareness through the social network. This study presents a personalized social network analysis by examining the information of more than 3000 tweets from a corporate social network account. Most essential metrics that helped more interaction and changed on tweets are posted time at the content of twits. As a result of our study, a time recommendation strategy for Twitter is proposed which increases user interaction. Therefore finding the most appropriate time for sharing will increases social media interactions such as sharing, likes and retweets. For this reason, sharing time estimation was made using multilayer perceptron (MLP) and group method of data handling (GMDH) neural networks methods with the dataset obtained from the tweeter. As a result of the study, it was determined that more successful results were obtained with 1.8% mean absolute percentage error (MAPE) using the GMDH method.
Improved Trigger Time of Force Sensorless Power-Assisted System based on Fuzzy Logical Strategy and EMG Signal

This paper aims to enhance the trigger time of the force sensorless power-assisted system based on fuzzy logical design and electromyogram (EMG) sensing information. The disturbance observer integrated into power-assisted systems is a recently conventional method for developing the force sensorless power-assisted system (PAS). However, the behavior of conventional observer algorithm is alike low-pass filter, perhaps the time-delay is occurred by the observer algorithm. Hence, the improved trigger time of the force sensorless power-assisted system based on the fuzzy logical strategy and EMG signal has been proposed to treat the time delay of a force sensorless PAS. The Slope Sign Changes algorithm is utilized for processing EMG signals and extracting the feature of the EMG sensing information. Further, the fuzzy logic model is appropriately designed to integrate with EMG signal and disturbance observer. Experimental results show that the presented method can properly reduce time-delay of a force sensorless PAS according to the proposed fuzzy logic strategy with EMG signals.
Machine Learning based Omnifood: Nutritional Meal Drop

There are many portable applications for weight and diet management available these days. Furthermore, there are few examples of food recommendation systems that provide nutritional information about reasonable food choices to the user. An enormous number of people are extraordinarily busy with their positions, loved ones, and other basic exercises, which doesn't take into account cooking. This could lead a poor diet routine and lasting health consequences. It is highly required to solve this issue by using a web-based methodology. Clients can use the developed web page to pick their eating routine and food sources they like and dislike, and our computation will make a custom and individual step by step weekly plan. The proposed web application guarantees the client to obtain all of the required enhancements and supplements, notwithstanding the eating routine. The proposed application works with the eateries and other cooking accomplices to truly get ready and convey all dinners from the delivered feast plans to chosen regions. This paper proposes Multinomial Naive Bayes and this proposed algorithm, classified for food reviews. The proposed research work has created a webpage using HTML5, CSS3, and JavaScript and classified food reviews using machine learning. Four different algorithms are executed on the proposed dataset, they are Bernoulli Naive Bayes, Gaussian Naive Bayes, Logistic Regression and Multinomial Naive Bayes. In algorithm evaluation, a performance matrix has been formed i.e., precision, recall and accuracy of Multinomial Naive Bayes are observed to be high when compared to other algorithms. So, the Multinomial Naive Bayes algorithm is utilized in the proposed model.
A Study of Service Registration and Discovery Techniques in Microservice Architecture

The microservice architecture has become a key technology in the development of the Internet. It brings a revolutionary development to the software production relationship and effectively improves the scalability and stability of the system. However, as the system evolves and the microservice splitting granularity is wrefined, the scale of microservices explodes and problems such as extended service discovery time and high service maintenance costs arise. For these problems, this paper proposes a detection mesh scheme (DM) based on service mesh and service semantics, which is divided into two main parts: service registration and service discovery. In terms of the service registration, based on the idea of a service mesh, the microservice governance and microservice development are deeply decoupled. In terms of the service discovery, the local-remote algorithm is proposed by considering the path, semantics, and state of microservices. In addition, a hierarchical ontology model is builded to provide a solid foundation for service registration and service discovery. Through experimental verification, compared with the web service discovery based on the maximum weighted bipartite graphs scheme(WSDMWBG), the DM scheme improves 12.71% in first hit rate and 28.3% in total microservice call time, effectively reducing the service discovery latency.
Static VAR Compensation Capacity for Bus Voltage Setting in Electric Power Systems

Under certain operating conditions, the bus voltage can drop below its minimum limit. A large load can cause the voltage drop on the bus, or the bus location is far from the generating center, so a transmission line connects the bus line also located quite far. A static capacitor bank can improve voltage quality. This capacitor will compensate for the reactive power flow so that the current flowing in the network becomes low, and this can improve the voltage. This study proposes a compensation technique to control the bus voltage so that it is at its security limit. The methodology uses power flow based on the Newton-Raphson technique because this technique has powerful convergence compared to Gauss-Seidel and Fast Decoupled Techniques and capacitor modeling. The simulation results on the IEEE 9-bus system show different compensation rates for buses, such as bus 5 of 33.7 MVAR and bus 8 of 12.6 MVAR. In addition, losses fell about 1.5%.
Order Function and Macroscopic Mutual Entrainment in Uniformly Coupled Limit-Cycle Oscillators

A concept of order function is proposed to develop a general self-consistent theory of mutual entrainment in large populations of limit-cycle oscillators such that each element is uniformly coupled to every other. The onset of entrainment is revealed to be a bifurcation of the order function in functional space. Numerical evidence for the theory is also presented
Data Classification Based on Broad Learning System with Hybrid Genes-PSO

Broad Learning Systems (BLS) have recently been shown to be effective and efficient. This paper reviews the basic structure of BLS and proposes a BLS based on improved PSO. The proposed model optimizes the parameter $\lambda$ in the ridge regression method used when the BLS directly computes the pseudo-inverse to obtain the network weights. In many problems, $\lambda$ is usually set as close to zero as possible, but in practice, different $\lambda$ values are used, and the accuracy of the model will be different. If a method can be found, and it is different from the tedious and complex grid analysis, it can find the value of $\lambda$ that makes the model as accurate as possible while taking into account the accuracy and efficiency, then the overall performance of the network can be improved. Finally, experimental results on multiple fraud and cancer datasets demonstrate, collected from the UCI repository, that this method is better than selected state-of-the-art methods, such as stacked autoencoders, deep belief networks, etc., in terms of accuracy and training speed. And the results of this paper also show the conclusion that the method of parameter selection can greatly decrease the training time of the traditional BLS on the classification scene on the UCI dataset.
Photonic microwave generation using period-one nonlinear dynamics of semiconductor lasers under highly asymmetric mutual injection

This study investigates an all-optical microwave generation approach using a semiconductor laser operating at period-one nonlinear dynamics. In particular, a novel all-optical stabilization scheme based on highly asymmetric mutual injection is proposed to improve the spectral purity of such generated microwaves. As a result, microwaves at a frequency of 64.3 GHz with a 3-dB linewidth below 2.7 kHz and a SPSR of 46 dB is generated. Microwave generation with high spectral purity over the V (40 to 75 GHz) and W (75 to 110 GHz) bands can be achieved by simply adjusting the power and frequency of optical injection.
Towards Real-time Semantic SLAM Based on Undesired Regions Segmentation

Visual SLAM (vSLAM) is an essential technology for providing robots with position and environment information in unknown environments. But the problem of robustness has not been well solved. For example, the features near the skyline will aggravate the scale drift of monocular SLAM systems, and the existence of challenging dynamic objects violates the static world assumption. In this paper, a robust vSLAM method to remove three undesired regions of the sky, skyline and potentially dynamic objects, building on ORB-SLAM2, is proposed. We incorporate a real-time semantic segmentation network into a vSLAM system to quickly obtain accurate semantic information to eliminate the influence of undesired regions on localization and mapping. In addition, for real-time efficiency, we put the semantic segmentation network in a separate thread and perform semantic segmentation only on keyframes. We conduct monocular and stereo SLAM experiments in 11 sequences (00–10) of the KITTI dataset. The evaluations show that our method achieves a 44.44% accuracy improvement over the baseline ORB-SLAM2 in the best case, and alleviates the scale drift in the monocular case. Compared with DynaSLAM, the advanced dynamic semantic SLAM, our method is slightly more accurate and much faster.
Autonomous Penetration Trajectory Control for Time-sensitive Targets and Dynamic Threats

Aiming at the optimization problem of the unmanned aerial vehicle (UAV) trajectory control in ground penetration combat, the 3-DOF particle model and dynamics model of UAV were established in combination with battlefield environment. Constraints such as initial conditions, terminal conditions and flight performance are considered. Considering the radar cross section (RCS), the dynamic RCS threat model is established. The trajectory optimization model of UAV ground penetration operation under the theoretical framework of optimal control rate was constructed, and the autonomous trajectory control of time-sensitive target under uncertain conditions was solved by combining the receding horizon control strategy. On this basis, taking the flight time and threat probability of UAV to combat area as the objective function, an adaptive weighted performance index and switching sub-objective function method are proposed, and the simulation is carried out. The results show that this method can optimize UAV penetration trajectory control more reasonably against time-sensitive targets in complex battlefield environment and multiple constraints, which is beneficial to realize attack mission.
Finite Element based Compression and Volumetric Load Analysis for Grasped Objects

Grasp evaluation criteria are not only restricted to the planning stage but also while the grasp is performed and are included as optimization parameters to select a suitable grasp. For fragile objects, a strength analysis is needed to determine damages of the object through an end effector. To evaluate this criterion, the emerging residual stresses for various grasping positions must be calculated. In this work we present a Finite Element based mechanical load analysis for a set of grasping positions. Because the grasping positions have a high spatial distribution the setup is dynamic and adaptable to various input parameters. Therefore, boundary conditions are automatically set through auxiliary objects to determine the consequences of clamping forces and volumetric loads on the grasped object. Through the inclusion of all possible mechanical loads we are able to perform a fully comprehensive strength analysis. Our evaluation for a reference object showed comparable displacements (<0.1%) to a manually specified strength analysis in Ansys Structural while significantly reducing the time to set up the strength analysis.
Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques

Stress can be described as an alteration in our body that can cause strain emotionally, physically, or psychologically. It is a reaction from our body to something that demands attention or exertion. It can be caused by various reasons depending on the physical or mental activity of the body. Commuting on a regular basis also acts as a source of stress. This research aims to explore the physiological effects of the commute with an application of a machine-learning algorithm. The data used in this research is collected from 45 healthy participants who commute to work on a regular basis. A multimodal dataset containing medical data like biosignals (heart rate, blood pressure, and EEG signal) plus responses obtained from the questionnaire PANAS. Evaluation is based on the performance metrics that include confusion matrix, ROC/AUC, and classification accuracy of the model. In this research, several machine learning algorithms are applied to design a model which can predict the effect of a commute. The results obtained from this research suggest that whether the interval of commute was small or large, there was a significant rise in stress levels including the bio-signals (electroencephalogram, blood pressure and heart rate) after the commute. The results obtained from the employed machine learning algorithms predict that heart rate difference before and after commute will correlate with EEG signals in participants who have self-reported to be stress after the commute. The random forest algorithm gave a very promising result with an accuracy of 91%, while the KNN and the SVM showed the accuracy of 78% and 80% respectively.
Distributed coordination of power generation and transmission for an energy network

This paper studies a modified economic dispatch problem (EDP) of an energy network when both power generation and transmission costs are taken into account. The considered network consists of power stations and energy consuming systems connecting via transmission lines. Due to the differences in generation costs and capacities of generators and the demand of consumers, it is highly required to have an optimal coordination in power generation distribution and power transmission. By assuming the cost functions have convex quadratic forms and applying Douglas–Rachford alternating direction method of multipliers (ADMM), we design a simple iteration update for each agent (corresponding to one power generators or one energy consumer systems) to determine the optimal power generation distribution (for generator nodes) and the optimal power transmitted to its neighbors in a distributed manner. The effectiveness of the proposed method is verified via numerical simulations for a modified IEEE 30-bus test network. We also remark that it can be used to solve the traditional EDP with a small modification in problem setup.
Analysis and Performance of Solar Photovoltaic Energy System in India: Case Study

There are seven types of renewable energy, of which only five will generate electricity. The most extensively utilized renewable energy source for residential usage is solar photovoltaic energy. With over 486GW of generation capacity, solar PV remains as the third largest renewable power source, with advanced photovoltaic (PV) innovation. The United States and China have the most solar plant installations. India holds the third place as the country's solar area is expected to recover completely after the COVID-19 epidemic, wherein the energy from solar PV is expected to overtake coal before 2040. To examine and observe the various processes carried out by solar PV, several experimental studies have been carried out. This research article presents the different case studies of solar PV systems and observes the characteristics of voltage and current or voltage and power for different solar radiations and temperature, respectively. Secondly, the proposed study has observed and analyzed the performance of PV module in series or parallel connections concerning I-V and V-P characteristics. In third section, the shading effects on solar PV module output power is observed. In the fourth and fifth sections, methods to solve the shading module's output power constraint has been studied and finally the MPP is observed by varying the duty cycle of the converter. In the sixth and seventh section, the performance of SEPIC converter is evaluated based on the open and closed-loop systems, and the challenges in buck-boost converter are solved.
Performance Improvement for Interior Permanent Magnet Machines by Winding Reconfiguration

Interior permanent magnet (IPM) machines have been widely employed in traction applications because of their significant advantages in power density and efficiency. In traction applications, it is essential to achieve speeds above nominal speed and operate in the flux-weakening region. In this paper, the winding reconfiguration method is proposed to improve the flux-weakening performance characteristics of IPM machines. An IPM machine, having the 2010 Toyota Prius IPM Machine's specifications, is designed to have two separate winding sets as “main” and “auxiliary” windings. Hence, it has become feasible to switch between different winding characteristics using power switches. The turns-changing method is employed to decrease the winding inductance by reducing coil turns, and the parallel branch method is employed to reduce the winding resistance. In this way, a high-torque at constant power region or high efficiency at constant torque region can be achieved. The influence of different operation modes, obtained by arranging the switching between windings, on the flux-weakening characteristics, including torque/speed, power/speed, and efficiency maps, has been investigated. The flux-weakening performance characteristics of the IPM machine with reconfigurable windings are calculated using a hybrid calculation method that combines finite-element analysis (FEA) with MATLAB. The flux-weakening performance characteristics of the IPM machine have been significantly enhanced thanks to the proposed winding changeover approach.