Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Fabrication of Low Cost Microfluidic Device for Synthesis of Silver Nanoparticles

This paper presents the design and fabrication of a polymethylmethacrylate (PMMA) microfluidic device for the formation of silver nanoparticles. The nanoparticles were prepared by reducing silver nitrate solution with sodium borohydride. A T-junction micromixer was designed and fabricated on the PMMA chip for rapid mixing of silver nitrate and sodium borohydride solutions. The effects of different flow rates and molar concentrations of silver nitrate solution on the formation of silver nanoparticles were investigated. These nanoparticles were characterized using UV-Vis spectroscopy.
Behavioral Analysis for User Satisfaction

Today's Web aims to increase engagement with the user, his or her emotions and behaviors. Contemplating the emotional sphere is potentially very useful when it comes to Web sites: understanding how the user feels can help the service provider understand his or her needs and improve the service he or she offers. This project aims to devise a behavioral feature extraction system to create an interactive system that can provide a personalized experience and maximize user satisfaction. The system contemplates behavioral analysis techniques using Machine Learning, Recommender System and Emotion Recognition algorithms. In the experimental phase, users tested the system through Expressing Mixed Emotions, SUS and SUPR-Q tasks and questionnaires with the aim of evaluating the User Experience and The Usability of the system.
U-Net Based Water Region Segmentation for LAPAN-A2 MSI

In this work, we analyzed the performance of a deep learning-based segmentation method in extracting water regions from multispectral imageries (MSI) taken by LAPANA2 microsatellite. The interested water regions include open seas and the river as well as their branches. The capability of detecting and segmenting the water component on LAPAN-A2 MSI is important as the satellite was dedicated to support maritime surveillance missions on Indonesian waters. Therefore, this capability will help future water object detection to encapsulate its region of interest, i.e., water. The segmentation has been performed by employing a state-of-the-art deep learning-based method, U-Net, using 696 training images. This method is considered due to its capability to provide promising accuracy without requiring an extremely extensive amount of training dataset. Based on our experiment, the trained U-Net has shown a satisfying result with an accuracy of 89.13% as measured using Intersection over Union (IoU) metric.
Domain Size Effect on Dielectric Properties of Barium Titanate Ceramics

Barium titanate (BaTiO3) ceramics with various grain sizes were prepared by a conventional sintering method and a two-step sintering method. The permittivity of the ceramics increased with decreasing the grain size down to 1.1 µm on average. The BaTiO3 ceramics with an average grain size of 1.1 µm had a high permittivity of 7,700. The transmission electron microscopy (TEM) observation revealed that the 90° domain density increased with decreasing the grain size. The domain size of the ceramics with the highest permittivity of 7,700 was approximately 100 nm. From an ultra wide range dielectric spectroscopy, it was found that the high domain density enhanced the orientational polarizability due to the domain-wall vibration and the ionic polarizability due to the lattice vibration. It was clarified that the increase of the permittivity with decreasing the grain size was due to the domain size effect.
Parameter optimization and stability prediction of the Dual-SLIP model using evolutionary algorithms and ANN

This work presents two computationally low-cost optimization algorithms to determine parameters for the Dual-SLIP model producing stable walking gaits. While previous approaches use exhaustive or random searches and techniques requiring long simulations typically running until a fall is detected or an end condition is met, the approaches presented here only require the simulation of a single step thus reducing the computation time. The proposed optimization algorithms are based on Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Also, an Artificial Neural Network (ANN) and a Deep Neural Network (DNN) are presented to predict the stability and the number of steps a given set of parameters will be able to produce. After training, these algorithms do not require any simulation of the walking model to yield their result. Simulation results show the efficacy of the proposed algo-rithms.
Investigating the Wavelength and Polarization of Electromagnetic Wave using Raspberry Pi Zero W

Two measurements concerning electromagnetic waves are presented. First measurement is devoted to the wavelength, the second one is to the polarization. In both cases the Raspberry Pi Zero W and Wi-Fi router at 2.4 GHz are used. In the first measurement two metal plates are arranged in a way to create standing wave to wavelength investigation. In the second measurement the plane of wires is arranged in a way to pass only particular polarization. Again, the Raspberry Pi Zero W is employed to investigate the polarization of electromagnetic wave. The suitability of measurements in the base laboratory course is approved.
Study on polarization properties of birefringent microstructure fiber with lateral pressure

Using a full-vector finite element method, the phase modal birefringence and group modal birefringence to lateral pressure alone slow axis and fast axis versus wavelength in birefringence microstructure fiber was analyzed. In the wave band of our research, 600nm-1700nm, when different direction pressure is applied, the phase modal birefringence (B) and group modal birefringence (G) have different change to wavelength in microstructure fiber. Moreover, the results reveal that the pressure sensitivity of B and G have different change to wavelength when applying different direction lateral pressure. Our research has great signification in designing microstructure fiber and using microstructure fiber in sensing field et.al., especially using in multidimensional sensor.
Multihead Causal Distilling Weighting Is All You Need for Uplift Modeling

Uplift modeling is a branch of predictive modeling technology, which is usually used to analyze marketing, advertising and product personalization experiments. In this kind of application scenario, we usually do a large number of random experiments to assist decision-making. And thus there is a great need for us to control the number of features as well as tell the most important features in order for the next steps. What's more, in this kind of scenario, the interpretability of features also becomes very important, which means that we should not only pursue the accuracy of prediction, but also minimize the difficulty of controlling the features. However, most existing studies only focus on the accuracy of model prediction, but ignore the cost of controlling and observing too many variables as well as the interpretability of features in practical application. In order to better solve this problem, we introduce a multi head weight calculation method based on causal inference. Instead of selecting features based on the result of machine learning, we manage to select features from the source with causal inference, a total different method from traditional machine learning methods. It can be viewed as a method to solve the problem of overfitting. In our experiment, we use the data from different industries and use different numbers of selected features to evaluate the effectiveness of the proposed feature selection method. The results show that our algorithm significantly improves the performance compared with the feature selection method in standard machine learning theory.
Improvement of Proactive Attitude by Alternating Enhancement of a Sense of Acceptance and Control

When people work collaboratively, they often communicate their task status to each other and point out problems. However, when the collaboration partner is a system or an artificial agent, the collaboration is often not a two-way interaction, but a one-way monitoring and instructional relationship. In this study, we propose a human-agent interaction model that synthetically and progressively encourages one's acceptance of the communication partner's behavior and their control over one's own behavior. By doing so, we aim to reduce users' mental workloads and improve their proactive attitude toward the task and the interaction. Active attitude is the attitude of voluntarily taking the initiative to engage with a subject, such as the task and the communication partner. We conducted an experiment to compare the proposed model with another interaction model that aims to support the decision-making of the participant. The results showed that 1) the proposed model improved participants' proactive attitude to some extent, 2) the proposed model encouraged interaction and improved the participant's sense of acceptance, and 3) participants who applied the proposed model when they interacted with the agent were able to reduce their mental workloads in the task.
Speech Emotion Recognition Using Energies in six bands and Multilayer Perceptron on RAVDESS Dataset

Emotional information has recently received in-creased attention in studies on speech signals. Selecting the ideal speech feature representation for speech emotion recognition (SER) is the most difficult task. According to the statistical study, the functions of each speech characteristic vary in various emotional states, suggesting that various features have varying capacities for emotion distinction. A feature extraction and classification technique to create a reliable recognition system is presented in this article. We also provide new features that are based on energy and energy distribution taken from the (RAVDESS) database. Based on the results of the experiments, it can be concluded that the suggested strategy, when compared to a direct (multi-class classification).
Innovative Turned and Collaborative Technology using Simulated IoT Applications

Distributed system allows for quick access to computing, stockpiling, and even connection. Yet, for systems and networks that are far from a consolidated public cloud or data centre source, these centralised architectures might cause delays and performance difficulties. If data analysis is concentrated in the cloud, for example, to analyse emergency circumstances, any connectivity failures or delays will hinder the function and endanger human life. However, the restricted availability of such edge servers thwarts this interesting proposal. The virtual edge computing concept is discussed in this study as a way forward. By remotely accessing all assets, particularly edge routers, and making them readily accessible through endpoints, virtual edge bridges the internet, periphery, and swamp worlds. Fog and cloud technologies architectures are intriguing solutions to cloud-based technologies, particularly for rapid reaction to crises can also be used to allow new microservices and collaborative remedies for pcs. This research study provides a comprehensive virtual frontier framework and some of the important research challenges that must be addressed to facilitate more pervasive and efficient better external.
Research on detection of surface defects of lamps with multi-color light sources

Automated optical inspection (AOI) is one of the most intuitive technology for defect inspection, which is amply utilized in various industrial products. Nevertheless, each inspected target has a different characteristic, for instance, transparency. The transparency object possesses a high ratio of light-transmitting and the ratio will vary depending on the angle of an emitting light source. With this property, the emitting light source may reflect and transmit in accordance with some complex parameters and the capability of the inspection will be highly affected. One of a crucial parameter is the lighting design in the inspection environment. In this study, we propose several sets of wavelengths of the lighting source for evaluation. The multiple colors and natural light are used as the lighting source to capture the defects on the lamps cover. Then the images are assembled into a data set, which is imported into the ResNet model based on the convolutional neural network (CNN) for deep learning training. This study shows the results and concludes that the blue light source can acquire the best result with the highest accuracy of defect detection.
State Detection of Electric Energy Metering Device Using Computer Neural Network

Itis to explore the detection of the state of electric energy metering device by computer neural network. 15 users as the analysis object, the combination of the artificial intelligence technology and the state detection of electric energy metering device; the neural network in the realization method of state detection of electric energy metering device; secondary processing of the collected data of the measurement automation system; two kinds of state detection technology scheme models of neural network were created. A theoretical analysis for the operation state of the electric energy metering device was performed. Through the real case analysis, the priority of the analysis results is I: user 1 and user 48; priority II: user 26, user 45, user 49, user 44; priority III: user 14, user 15, user 20, user 25, user 28, user 30, user 37; priority IV: user 22, user 34. The comprehensive evaluation of the state detection of the electric energy metering device is completed, which further verifies the feasibility of the implementation of the research results and ensures the safe, accurate, stable, and reliable operation of the electric energy metering device.
Speech Emotion Recognition Using Deep Neural Networks

Emotion recognition in speech has received a lot of attention in the field of psychology and cognitive science, but in recent generations, data science has given Speech emotion recognition (SER) a lot of growth with particularly active and motivating aspect of human-machine interaction in speech communication. It can be used in an automatic remote call center, a car board system, the field of E-learning, and the emotions of students during the lecture. To obtain sentiments from audio signals, researchers have been developing a handful of well-known approaches for voice computations and classification methods. In this paper, a broad sense of overview of SER has developed using deep learning techniques such as audio signal preprocessing, feature extraction and selection methods and finally determining the accuracy of appropriate classifier. The emotional datasets Ravdess, Crema-D, Tess and Savee are concatenated and was used to train the one-dimensional Convolutional Neural Network (CNN). On concatenated datasets of Ravdess, Crema-D, Tess, and Savee, the feature combination of ZCR+energy+entropy of energy+RMS+MFCC have demonstrated the accurateness of 92.62%.
The Development of Music Recommendation Systems Based on Song Characteristics

This research constructed four music recommendation systems based on the combination of sentiments in a specific pieces of lyrics, the semantic difference of words within specific pieces of lyrics (with two more specific types, one using Word Mover's Distance and one simply utilizing the Doc2Vector method), and a generalized approach finding similar vectors between various pieces of music containing the musician, genre, and lyric content. The last method has been applied to multiple participants and five of which (that filled out the survey) gave an average rating of 93.8 out of 100 for the general quality of recommendation.
Area Coverage of Swarm Robotics Based on Anti-Flocking Framework with Dynamical Clustering

Using swarm robots to deal with the task of area coverage has broad applications from regional surveillance, material transportation, to search and rescue. To achieve the goal of higher coverage rate and lower time cost, we present the K-means clustering strategy to execute the dynamical selection of the target position of the next step for each robot, based on the anti-flocking framework. The proposed strategy clusters the non-explored area points to distribute the covering task for each robot and finally assists the swarm to cover the area efficiently. Compared with the classical anti-flocking algorithm, the coverage rate of the proposed method in this paper has been significantly improved. For different combinations of swarm size and interaction radius, this paper uses the genetic algorithm to optimize the model parameters and obtain the optimal model parameters. The simulation results prove that when the model parameters are all optimal, the larger the swarm size is, the more efficient the swarm coverage is, but the interaction radius has no significant effect on the efficiency of the swarm coverage. Finally, we successfully transfer the dynamical clustering algorithm to a semi-physical simulation platform and implement our method adaption in a hardware-in-the-loop simulation to achieve swarm coverage with 7 quadrotors.
DQN-based Reinforcement Learning for Vehicle Control of Autonomous Vehicles Interacting With Pedestrians

Autonomous Vehicles (AVs) have become a popular research topic in recent years due to their ability to improve road safety by reducing traffic accidents and human injuries. Vehicle control is the most significant part of autonomous driving, which adjusts the steering angle and velocity of AVs during driving. Recently, vehicle control has seen consequential progress using effective Artificial Intelligence (AI), especially Deep Learning (DL) techniques. Recent works have been limited to using Reinforcement Learning (RL) techniques to control AV to follow only its path without taking into consideration other road users, especially the pedestrians. In this paper, we propose a Novel Reinforcement Learning based model using Deep-Q Networks to control the AV in a complex scenario involving vehicles and pedestrians. AV learns the policy of several actions in order to reach its destination without accidents with other road participants. Our approach in tested and validated using the CARLA simulator. Our results show that the proposed approach achieves better performances in terms of average reward, success rate, and collision rate over time.
An Adaptive Robust Nonlinear Control Approach of a Quadcopter with Disturbance Observer

Unmanned aerial vehicles (UAVs, drones) have become one of the key machines/tools of the modern world in which they are widely employed to effectively enhance working performances in many fields of daily social life and manufacturing such as delivery, protecting wildlife, agricultural activities, academy, searching, rescue missions and military applications. To accomplish the given mission, the systems are required precise controllers with strong ability of adaptation and robustness. In this article, we present an adaptive robust nonlinear controller for position tracking control problems of a quadcopter system. The controller is structured with two control loops. In the inner loop, the attitude of the system is adjusted following desired signals using a proper combination of sliding-mode-backstepping control framework under nonlinear disturbance observers. The position control mission is realized by another nonlinear altitude control method. A new gain-learning mechanism is then proposed to improve both transient and steady-state control performances. Stability of the closed-loop system under time-varying disturbances is governed by Lyapunov theories. Effectiveness and feasibility of the proposed control approach were verified by comparative simulations.
Machine Learning Techniques for Predicting UAV Design Parameters: Overview, Methodology and Potential Benefits

Estimating the performance parameters of Unmanned Aerial Vehicle (UAV) in early concept appraisal is complicated due to wide variety of mission types and corresponding flight profiles. Empirical correlations between fundamental parameters can provide initial characteristics but to explore the overall design, a comprehensive time-consuming trade-off analysis is required to reach the jumping-off point. This paper explores the potential that machine learning approach can offer in terms of extracting knowledge from UAVs already in service and making quick predictions of UAV design parameters in conceptual phase. For this purpose, an ML framework based on five different regression models is formulated using database of fixed-wing UAVs key design attributes. The predictive performance of the regression models shows excellent agreement with the actual values and comparatively, turned out to be more accurate than the estimations obtained from empirical correlations. Overall, the ML approach has a great potential for being applied as a surrogate model for evaluating novel UAV design concepts in less computational time and cost.
URL-Based Dynamic Monitoring of Android Malware using Machine Learning

Android malware depend heavily on DNS traffic to provide flexible communications between compromised smart devices and malicious infrastructure. Nevertheless, we found that there is a gap between connecting DNS queries requests by smart devices and the logs at the DNS-service network level to work altogether. So, this paper combines and correlates two approaches: top-down detection by identifying malware domains using DNS traces at the network level, and bottom-up detection at the device level using the dynamic analysis in order to capture the URLs requested on a number of applications to pinpoint the malware. Here, we propose a URL-based dynamic monitoring of Android malware, with two parts, namely: (i) a client side which is a DNS sniffer that captures and classifies (through the usage of blacklists) the URL(s) requested by app(s) under scrutiny running on the smart device, and (ii) a central server where these URLs are collected and classified by using a machine learning algorithms and where a visualization takes place of the most malware attacks detected. Addditionally, the malicious URLs discovered are used in order to carry out a string pattern matching search into the DNS logs of DSN servers to pinpoint another infected device. Our experimental results with the proposed monitoring system using the Random Forest algorithm show, regarding the F1-Score, a better performance than the previous works with Decision Tree algorithm such as J.48 classifier.
A serious game for cognitive and fine motor skill rehabilitation: a qualitative evaluation.

Cognition and upper motor skills are some of the most common areas in rehabilitation of various pathologies; cognitive recovery can be supported on various areas as inhibition and object recognition, while motor skills require repetitive exercises, these make them the perfect target to develop serious games for catching the attention of patients and make easier for them to complete different tasks. This paper will show a qualitative usability evaluation of a serious game, developed for rehabilitation of cognition and fine motor skills. The game was built according to State-of-the-art determined design patterns as color, border definition and sound feedback, meanwhile the dynamics were chosen to match cognitive inhibition and object recognition recovery, implementing repetitive grasp and release tasks to enhanced motor abilities. At the end a usability study, based on Likert scale to estimate user’s opinion was carried out through a survey and applied to 21 physiotherapy and medicine students; concluding a good grade of usability, usefulness, playability and applicability of the application.