Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of Uber & Ola using Deep Learning

Sentiments are the basic emotions of the human and understanding it for various problems is just like a feature extraction, where different sentiments are taken into consideration for finding problems and understanding the use case. For understanding the sentiments there are various methods, in this, the sentiment analysis is done based on twitter which uses Uber & Ola, which are part of cab services. Understanding the needs and complaints of cab service can help in boosting and solving various problems related to users that are tweeted. Deep learning plays an important role in using tweets and extracting information. Google Word2Vec is also used to generate the vocabularies between the words and make them understand the similarity, which in turn will give better outputs for the tweets. The tweets are categorized in Positive & Negative. The two deep learning algorithms used for sentimental analysis are part of a multi-layer perceptron, which are Deep Feed Forward Neural Network and Convolution Neural Network. The algorithms are trained on training data and accuracy is generated to find if it was trained properly or not. The testing data is used to check if it predicts the correct output which will tell if the algorithm is suited for the datasets or not. The programming language used for sentimental analysis is none other than python.
Evaluation of Autism Classification Using Machine Learning Techniques

Autism is a development disorder that impacts the social interaction, cognitive abilities and attention of children. The child's caretaker can identify the autistic behavior visually when a child displays repetitive behaviors, limited eye contacts, limited facial expressions and no signs of imitation. The proposed work uses GLCM and SVM for classifying autism and normal controls. GLCM is used for feature extraction from thermal face images and SVM is used for the classification process. The Feature extraction is done to extract the features like contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, entropy, homogeneity, maximum probability, a sum of squares, variance and autocorrelations. Machine learning algorithm GLCM and SVM are trained and validated with 40 thermal images of different subjects. Through various experiments, the classification sensitivity and specificity are found to be 66.58% and 91% respectively. The accuracy level and normal subjects are 86.73 and autism subjects 85.30 respectively by using GLCM and SVM.
Maximizing Network Resource Utilization Based Server Deployment Algorithm in LTE-Edge Computing

In an edge computing network environment, in order to improve the utilization of network resources, this paper proposes a server deployment algorithm for maximizing network resource utilization under edge computing. First, the problem is modeled from four aspects: network topology, user service requests, storage and computing resource allocation, and service node traffic usage matrix. Secondly, an objective function for minimizing network traffic is constructed, and a server deployment algorithm for maximizing network resource utilization under edge computing is proposed to solve. The algorithm uses the idea of slack constraints to solve the optimal server deployment strategy. Use tabu genetic search algorithm to find the optimal storage node and compute node deployment location. Use Lagrange operator to solve the best set of service nodes. In the experimental part, it is verified that the proposed algorithm achieves better results in reducing network traffic.
Analysis on State of Art Relationship between Compilers and Multi-Core Processor

In this paper, we have discussed compiler optimization on different multicore processor architecture. General architecture of an optimizing compiler has been prescribed, Structure of multicore processor is elaborated with the help of architectural components. GCC (GNU Compiler Collection) and ICC two compilers are being observed for two languages i.e. C/C++. Optimization level for both of these compilers i.e. ICC and GCC are discussed on multiple flags. Their represent goal to compare the impact of various compiler optimizations on program performance using two compiler i.e. GNU C Compiler and ICC Compiler. Different techniques like Low-level SIMD intrinsic, Code scheduling, PARSEC Benchmarks, OOP for embedded system and compiler optimization and multi-objectives scheduling algorithm based on cuckoo optimization are observed and their objectives and limitations are highlighted as state of art.
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation

Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world. An important part of communication, however, takes place at the non-propositional level (e.g., politeness, formality, emotions), and it is far from clear whether current MT methods properly translate this information. This paper investigates the specific hypothesis that the non-propositional level of emotions is at least partially lost in MT. We carry out a number of experiments in a back-translation setup and establish that (1) emotions are indeed partially lost during translation; (2) this tendency can be reversed almost completely with a simple re-ranking approach informed by an emotion classifier, taking advantage of diversity in the n-best list; (3) the re-ranking approach can also be applied to change emotions, obtaining a model for emotion style transfer. An in-depth qualitative analysis reveals that there are recurring linguistic changes through which emotions are toned down or amplified, such as change of modality.
A Multi-method Approach for Requirements Elicitation for the Design and Development of Smartphone Applications for Older Adults

The proportion of older adults (OAs) is increasing throughout the globe. One of the most important needs of this age group is to maintain active social engagement, which can be achieved through smartphone applications offering active social engagement facilities. But OAs are often reluctant to use these apps, which leads to lower uptake over time. The underlying problem is pitfalls in guidance to develop smartphone applications for older adults. This leads to a lack of usage of the guidance by practitioners and hence the reflection of these guidelines on apps for older adults is limited. We have researched this problem space by undertaking a mixed-methods empirical study with 235 OAs. We elicited requirements that can be used in the design and development of smartphone applications for older adults. The contribution of this study is an evaluated classification of the derived recommendations, called ReDEAP (Recommendations for DEveloping smartphone applications for an aging Population). ReDEAP serves as a starting point for researchers and practitioners designing smartphone applications for older adults.
Adaptive Fault-Tolerant Control of a Quadrotor Helicopter Based on Sliding Mode Control and Radial Basis Function Neural Network

In this paper, an adaptive fault-tolerant control strategy is proposed for a quadrotor helicopter in the presence of actuator faults and model uncertainties by integrating sliding mode control and radial basis function neural network. By assuming knowledge of the bounds on external disturbances, a baseline sliding mode control is first designed to achieve the desired system tracking performance and retain insensitive to disturbances. Then, regarding actuator faults and model uncertainties of the quadrotor helicopter, neural adaptive control schemes are constructed and incorporated into the baseline sliding mode control to deal with them. Finally, a series of simulation tests are conducted to validate the effectiveness of the proposed control strategy where a quadrotor helicopter is subject to inertial moment variations and different level of actuator faults. The capability of the proposed control strategy is confirmed and verified by the demonstrated results.
Design and Implementation of Perturb & Observe MPPT Algorithm under Partial Shading Conditions (PSC) for DC-DC Boost Converter by Simulation analysis

In this paper, the Partial Shading Condition (PSC) in a solar panel with a dc/dc converter is presented with an improved MPPT algorithm. The irregular nature of the output from the Solar PV is controlled and linearized using the proposed P&O method of MPPT. However, the traditional MPPT uses the Particle Swarm Optimization (PSO) which was not much effective in PSC. In PSO the operation under PSC cannot be implemented where the global peak and local peak cannot be operated because the power flow will be constant with less efficiency. In P&O the existence of global peak and the local peak will achieve to operate with better efficiency compared to PSC. The analysis of the proposed system is proved mathematically and the results are discussed. To confirm the benefits, the proposed topology is modeled by utilizing MATLAB/Simulink software for a power rating of 1kW.
Predictability of Vibration Loads From Experimental Data by Means of Reduced Vehicle Models and Machine Learning

Nowadays electric cars are in the spotlight of automotive research. In this context we consider data based approaches as tools to improve and facilitate the car design process. Hereby, we address the challenge of vibration load prediction for electric cars using neural network based machine learning (ML), a data-based frequency response function approach, and a hybrid combined model. We extensively study the challenging case of vibration load prediction of car components, such as the traction battery of an electric car. We show using experimental data from Fiat 500e and VWeGolf cars that the proposed ML approach is able to outperform the classical model estimation by means of ARX and ARMAX models. Moreover, we evaluate the performance of a hybrid-ML concept for combination of ML and ARMAX. Our promising results motivate further research in the field of vibration load prediction using machine learning based approaches in order to facilitate design processes.
State and parameter estimation of suspended load using quadrotor onboard sensors

In this paper, we address the problem of state and parameter estimation of a suspended load using quadrotor onboard sensors. Flying with a suspended load alters the quadrotor flight dynamics, sometimes to a large extent, making it a challenging and hazardous task. Monitoring the state of the suspended load is vital for safe flight operations while parameter estimation decouples the control design from specific parameter-dependent solutions. We take advantage of the fact that the forces and torques the suspended load exerts on the quadrotor can be detected in the aircraft IMU measurements as a low frequency harmonic. Thus, by combining the available measurements and system mass we are able to estimate the state of the suspended load. Since our approach stems from understanding the aircraft-load interaction, we start off by delineating the full system model of the quadrotor with a suspended load. To isolate the natural frequency of the suspended load and determine the length of suspension cable, we employ the Fast Fourier Transform (FFT). The proposed estimation algorithms are validated through extensive numerical simulations and experimentally.
Diabetes Type 2 as a Risk Factor of Neurodegeneration Development and Cognitive Impairment in db/db Mice

Diabetes T2 with insulin resistance is a serious disease all over the world with a tendency to steady increase the number of cases and a risk factor of neurodegenerative diseases development, among them firstly Alzheimer’s disease. The aim of this study was to investigate behavior and general characteristic of genetic model of diabetes T2 mice db/db, trying to reveal beginning of development early symptoms of neurodegeneration development and characteristic of symptoms of diabetes T2 during treatment by liraglutide or autophagy inducer trehalose. Mice age was 3 and 5 months. Db/db mice had an increase in body weight, which progressed with age, a decrease in brain mass. Blood glucose levels were increased in db/db mice. Treatment with trehalose or liraglutide reduces its level. Db/db mice were characterized by decrease of overall orientation-exploratory and locomotor activities, increase in anxiety in the open field test. Liraglutide treatment showed positive change in open field test in db/db mice. Passive avoidance test revealed significant decrease in motivation, locomotor and exploratory activity, decreased learning in db/db mice. Db/db mice of both ages were characterized by an increase in the relative number of PMN and monocytes and a decrease in the number of lymphocytes, indicating an inflammatory response. Treatment with trehalose or liraglutide restored these indicators. Thus, the identified behavioral changes in db/db mice reflected the development of neurodegeneration signs. Some positive effects of liraglutide on behavioral processes have been shown. Trehalose and liraglutide reduced blood glucose levels and the severity of the inflammatory response.
Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this article proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>ha<sup>−1</sup> to 26.8 Mg<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>ha<sup>−1</sup>. The difference tests showed that the changes were extremely significant (<inline-formula> <tex-math notation="LaTeX">$p = 0$ </tex-math></inline-formula>). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.
Software Engineering Issues: An exploratory study into the development of Health Information Systems for people with Mild Intellectual and Developmental Disability

Persons with intellectual and developmental disability remain among the most vulnerable members of society and frequently face numerous barriers. This includes having poor access to health information systems (HIS), which may be further compromised by a lack of accessible HIS resources. To be more effective, HIS must be easily accessible and usable to be understood by users. The study explores the usability and accessibility of HIS that is used and accessed by people with mild intellectual and developmental disability. Through focus groups following which we drew on qualitative data, we explored and observed the accessibility and usability of HIS by men with mild intellectual and developmental disabilities aged between 18 and 35 years. The findings of our study show that there is substantial promise for using and accessing HIS by participants. However, we identified many barriers against and enablers for using and accessing these systems. We also identified some design requirements for developing health information system for persons with mild intellectual and developmental disability.
A New Compact Power Divider Based on Capacitor Central Loaded Coupled Microstrip Line

A new power divider is proposed with a compact size. In 50- $\Omega $ systems, impedance transformation is of great importance for power dividers. Coupled microstrip lines with capacitor loaded and two short-circuited ports are introduced to properly transform impedance and increase degrees of freedom in power divider designs. Image impedance and image transfer constant of coupled microstrip lines with capacitor loaded are derived. The main structural parameters of the power divider are calculated accordingly. A special design procedure is illustrated for the proposed power divider with the capacitor loaded, which is implemented on a printed circuit board. A good agreement between the simulated model and the fabricated sample is observed, which demonstrates the validity of the proposed circuit model and the design scenario.
SeedEZTM Interdigitated Electrodes and Multifunctional Layered Biosensor Composites (MLBCs): A Paradigm Shift in the Development of In Vitro BioMicrosystems

We developed a new technology for the realization of composite biosensor systems, capable of measuring electrical and electrophysiological signals from electrogenic cells, using SeedEZTM 3D cell culture-scaffold material. This represents a paradigm-shift for BioMEMS processing; ‘Biology-Microfabrication’ versus the standard ‘Microfabrication-Biology’ approach. An Interdigitated Electrode (IDE) developed on the 3D cell-scaffold was used to successfully monitor acute cardiomyocyte growth and controlled population decline. We have further characterized processability of the 3D scaffold, demonstrated long-term biocompatibility of the scaffold with various cell lines and developed a multifunctional layered biosensor composites (MLBCs) using SeedEZTM and other biocompatible substrates for future multilayer sensor integration. [2020-0092]
A Stream Clustering Algorithm of Self-Adaptive Method

In this paper, based on the grid clustering-based streaming clustering algorithm, an in-depth study is performed. For the problems existing in the D-Stream algorithm, an adaptive method-based streaming clustering algorithm AD-Stream algorithm is proposed. The algorithm proposed in this paper selects two density thresholds, the dense grid threshold and the sparse grid threshold, based on the idea of data dispersion. As the data continues to enter, the grid density is constantly changing. The density threshold is adaptively adjusted for the smaller difference of the centralized data points has a good division effect, thereby improving the clustering accuracy of the algorithm, and dynamically adjust the next grid inspection time gap according to the two density thresholds currently detected. Through the analysis of the experimental results, it is verified that the algorithm has a certain improvement in the operating efficiency and clustering accuracy compared with the previous algorithm.
Efficient User Clustering Using a Low-Complexity Artificial Neural Network (ANN) for 5G NOMA Systems

Non-orthogonal multiple access (NOMA) has gained considerable interest from the 3GPP community as a potential radio access strategy for the future fifth-generation (5G) wireless networks. Compared to orthogonal multiple access (OMA), NOMA is more efficient from the perspective of throughput performance making it more favorable for 5G systems. Existing NOMA techniques merely offer a rigid user grouping without exploring channel heterogeneity and diversity to cluster users, resulting in a poor throughput performance. An adaptive user clustering (AUC) approach has been proposed to search through all possible combinations to obtain the best clusters with the highest throughput. This scheme exploits the channel diversity of users to maximize throughput, however, the brute-force search (B-FS) method to find the optimal combinations results in a prohibitive complexity. In this paper, a novel artificial neural network (ANN) approach is proposed for user clustering in the downlink of the 5G NOMA system in order to maximize throughput performance at an acceptable complexity. In the proposed strategy, ANN model is first trained with the historical dataset, which contains the transmitting powers and channel gains of the downlink NOMA users, along with the information of the corresponding clusters which maximize the throughput performance of the system. Next, validation is performed to tune the values of hyper-parameters such as learning rate, length of training data, and epoch learned during training to validate cluster formation and to avoid over-fitting of the model. Finally, the ANN model is tested with the learned parameters and tuned hyper-parameters, to predict the formation of clusters and to evaluate the accuracy of the model. Simulation results demonstrate that the proposed scheme is able to obtain a significant reduction in terms of complexity with a performance of 98% for throughput (near-optimal throughput performance) when compared with the optimal approaches.
Time-Domain Analysis of a Novel Co-Resonant Cantilever MEMS Sensor and Derivation of a New Measurement Concept Based on Beating

Co-resonant coupling can be employed to significantly enhance the sensitivity of dynamic-mode cantilever MEMS sensors. In applications, the resonance frequency, amplitude or phase of the sensor is usually tracked. Here, we focus on the analysis of the time-domain behavior of the co-resonant system as that allows new insights into the highly dynamic behavior of the coupled system and the derivation of a new measurement approach based on beating. The modelling approach by numerical SPICE simulations as well as closed analytical solutions based on differential equations are presented. For validation, the results are compared to experimentally obtained sensor data. [2020-0076]
Survey of DC-DC Non-Isolated Topologies for Unidirectional Power Flow in Fuel Cell Vehicles

The automobile companies are focusing on recent technologies such as growing Hydrogen (H2) and Fuel Cell (FC) Vehicular Power Train (VPT) to improve the Tank-To-Wheel (TTW) efficiency. Benefits, the lower cost, ‘Eco’ friendly, zero-emission and high-power capacity, etc. In the power train of fuel cell vehicles, the DC-DC power converters play a vital role to boost the fuel cell stack voltage. Hence, satisfy the demand of the motor and transmission in the vehicles. Several DC-DC converter topologies have proposed for various vehicular applications like fuel cell, battery, and renewable energy fed hybrid vehicles etc. Most cases, the DC-DC power converters are viable and cost-effective solutions for FC-VPT with reduced size and increased efficiency. This article describes the state-of-the-art in unidirectional non-isolated DC-DC Multistage Power Converter (MPC) topologies for FC-VPT application. The paper presented the comprehensive review, comparison of different topologies and stated the suitability for different vehicular applications. This article also discusses the DC-DC MPC applications more specific to the power train of a small vehicle to large vehicles (bus, trucks etc.). Further, the advantages and disadvantages pointed out with the prominent features for converters. Finally, the classification of the DC-DC converters, its challenges, and applications for FC technology is presented in the review article as state-of-the-art in research.