Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimum Location of SVC in an IEEE 33 Bus Radial Distribution System Using Power Sensitivity Index

Voltage stability improvement is the burning issue in the present scenario due to an increase in the demand for electric power. Thus the most challenging task for the power engineers is to maintain the voltage stability and the voltage level at all important buses during stress condition in the power network. A number of different works have been reported by the previous researcher for voltage stability improvement using the Static Var Compensator (SVC). In the present work, an IEEE 33 nodes radial distribution system is considered and the voltage of each bus is taken care with the help of power sensitivity index. The present work depicts the optimum location of SVC which is a part of Flexible Alternating Current Transmission System (FACTS) device. This will improve voltage stability and Power flow of the entire power system network to a great extent. This work is justified with the help of Power System Analysis Toolbox (PSAT) toolbox in MATLAB/Simulink 2016a.
Research on the Indicator System of Electricity Resources Purchase and Sale in China's Power Market

China's economy is experiencing a real situation of slowing growth, this has a major impact on the development of electricity demand. It is urgent to ask the power trading center to have an in-depth understanding of the development and changes in the purchase and sale of electricity resources. Through research and analysis of domestic and international power system indicator system. Starting from these resource characteristics, the paper comprehensively sorts and summarizes China's electricity purchase and power sales resources, and proposes an indicator system for purchasing and selling electricity resources that suits China's business, providing a scientific basis for the refined management of the power market.
Sentiment Analysis of Posts and Comments in the Accounts of Russian Politicians on the Social Network

Russian politicians are increasingly using social networks publishing a lot of texts. One of the important issues in the context of the analysis of political online communication is the choice of negative and positive topics in publications as well as the reaction of the audience. In order to analyze the main patterns of this process we have collected the data from the social network Vkontakte. Our sample covers the period from 1 January, 2017 to 25 April, 2019, in total 46293 posts and 2197063 comments in 23 politician’s accounts. To build the classifier we used two text corpora: Rubtsova’s corpus and RuSentiment corpus. The algorithm of sentiment analysis was implemented on the basis of bidirectional recurrent neural network. Using Rubtsova’s corpus we provided the accuracy of 91% and using RuSentiment we provided the accuracy of 84% (accuracy is calculated as the proportion of correctly identified cases from the test sample). We found that the markup of data significantly differs when different corpora were used. The most adequate results in the analysis of posts and comments, in our opinion, are obtained by using an ensemble of models based on the both corpora. As a result of classification, we identified a number of patterns. Thus, the number of likes and views of posts is higher for the posts classified as positive, and the number of reposts is higher for the posts classified as negative. We also found that the number of comments is higher for the posts with a negative sentiment, and the average sentiment of comments on positive posts is more positive than the average sentiment of comments on negative posts.
Ohmic InP/Si Direct Bonding for Multijunction Solar Cell Applications

Direct-bonded ohmic InP / Si heterostructures are realized for the fabrication of lattice-mismatched multijunction solar cells and replacement of compound semiconductor substrates with silicon substrates. We have examined the dependence of electrical conductivity on bonding temperature and doping polarity, and obtained ohmic InP/Si heterointerfaces. Moreover, we have fabricated InP solar cells on Si and verified their photovoltaic operation.
C-Band Simulations of Melting Icebergs Using GRECOSAR and an EM Model: Varying Wind Conditions at Lower Beam Mode

This article presents an electromagnetic backscatter model of iceberg and compares the modeled scattering behavior with C-band RADARSAT-2 synthetic aperture radar (SAR) images. It also explores iceberg SAR signature variability over various ocean parameters. Three-dimensional (3-D) profiles of icebergs were captured in a field study off the coast of Bonavista, NL, Canada, in June 2017 at the time of an SAR satellite overpass. The 3-D profiles were captured from a vessel, using a LiDAR and multibeam sonar. The SAR image and 3-D profiles were captured within hours of one another. Simulated SAR images of the icebergs were generated in a simulator called GRECOSAR with the satellite, target orientation, and ocean parameters that closely mimic the real SAR scene. A new ocean model was introduced to model an ocean backscatter at satellites’ lower incidence angle beam mode. Comparison between real and simulated SAR images of the icebergs shows good agreement in terms of SAR signature, total radar cross section, and polarimetric decomposition. Wind direction was varied over 90° extent to observe icebergs’ backscatter variability in the simulator. Furthermore, simulated SAR images were generated for low and high wind conditions. Our study finds that the macrostructure of the melt iceberg dominates its polarimetric behavior of its backscatter. Large variability of iceberg SAR signature over varying ocean parameters was also observed. A mathematical model that considers the melting condition of iceberg suggested that significant backscattering can reflect from top surface when the melt water layer could be as little as 0.1 mm.
Exploiting Symmetries in the Variational Meshless Method for 3-D Inhomogeneous Cavities

The variational meshless method (VMM) is a novel numerical algorithm that combines the intrinsic advantages of the meshless method based on the use of radial basis functions (RBFs) with the reliability of the variational formulation of electromagnetic boundary problems. It has been originally proposed and demonstrated in the analysis of 2-D structures, both homogeneous and inhomogeneous, aiming at the determination of the mode spectrum and the dispersion diagram. In this article, the VMM is extended to the calculation of the resonant modes of 3-D inhomogeneous cavities. The full theory is presented with implementation details. Moreover, the exploitation of symmetries is also discussed, which significantly speedup the method. Some examples are reported, and the results of the VMM are compared against either analytical values (when available) or commercial numerical codes based on the finite element method (FEM). In all cases, the VMM provides a large number of resonant modes with a limited number of unknowns, exhibiting high accuracy in short computing time.
Full-scale Personality Prediction on VKontakte Social Network and its Applications

In this work, we present our prediction results for 17 psychological scales based on online data from the Russian social network VKontakte. As part of this project, we predicted a variety of psychological traits, starting with personality traits from the OCEAN inventory and ending with Raven’s Matrices. We then introduced 2 models based on the source of digital data: the first consisting of semi-structured social profiles and the second of public page subscriptions. These models cover most public non-textual data on VKontakte. Lastly, we applied this model to real data with a view to construct psychographics for the audiences of different brands.
TexAnASD: Text Analytics for ASD Risk Gene Predictions

Autism Spectrum Disorder (ASD) is an extreme neurodevelopmental disease affecting 1 in every 59 children in the United States, and approximately 1% of US population. The clinical traits of the disorder include noticeable deficits in social interactions, language development and in many cases very narrow and repetitive interests and behaviors. ASD is highly heritable genetic disease, but the known causes including the biomarkers associated with ASD form only the tip of the iceberg. Over the past decade extensive research on exome sequences revealed only about one hundred genetic biomarkers with very high confidence. Number of putative ASD causing genes is rapidly growing with the advent of new technologies while researchers are struggling now to assess which genes are the true causing genes. Manual curation of each of the long list of genes is a cumbersome process that requires huge amount of expert work-hours, and is therefore expensive. An in silico prediction method can assist the human experts to check only a short-list of genes that were filtered through a machine learning system. Most of the existing ASD gene prediction algorithms involve high-performance computing platform to analyze large-scale genetic data which is counter-intuitive to the actual benefit of using an in silico method in the first place. We propose TexAnASD, a text analytics based ASD gene prediction algorithm that only utilizes what we know about each gene that we learn from published literatures. The proposed method outperforms most of the state-of-the-art ASD associated gene prediction methods. Moreover, the method offers an inexpensive model than those of the other competing solutions in terms of computational complexity and running time. All source codes, dataset, predictions and functional insights are available at http://ml.cse. ucdenver.edu/research/TexAnASD.
Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China Using Ultra-Low Frame Rate Videos

Traffic congestion detection plays an important role for road management. However, it is difficult to automatically report traffic congestion when it occurs in large-scale road network. One of key challenges for rapidly and precisely identifying early congestion is huge variations in appearance caused by illumination, weather, camera settings and other traffic conditions. To address it, we proposed a traffic-oriented model to classify congestion from large dataset of ultra-low frame rate video captured from traffic surveillance system. The proposed deeply supervised traffic congestion detector has two modules: attention proposal module and deeply supervised inception network. Specifically, within the shallow layers, the binary edge/corner density features are used in attention proposal module to generate the rang of interest (ROI) mask automatically. This strategy keeps the training process focusing on the congestion features without disturbances. Following the attention proposal module, a very deep structure based on the inception network was used together to effectively extract rich and discriminative features then detect traffic congestion. The approach was tested on a self-established dataset based on empirical data, which contains images captured from 14470 surveillance cameras for monitoring 5,215 km of freeway in Shaanxi province, China. The experimental results show that the accuracy of the proposed method could reach 95.77% considering various disturbances, conditions and other limitations, which is improved than unsupervised networks.
Predicting Cancer Association of LncRNAs by Co-Expression

Most genes coding for long non-coding RNAs (lncRNAs) have unknown function. There is therefore a strong interest in approaches for function prediction of such genes. One possible approach is to hypothesize that genes with similar patterns of gene expression across multiple conditions may be involved in similar processes, which can be identified by enrichment analysis on gene annotations. This paper describes an implementation of this approach, tested on lncRNAs associated with cancer. The paper describes a submission for The LncRNA Function Prediction Challenge at the BIBM 2019 workshop on lncRNAs.
A Large-Scale Benchmark for Vehicle Logo Recognition

The Vehicle Logo Recognition (VLR) is becoming more and more important in the Automated Vehicle Classification (AVC) research area since it can effectively assist vehicle classification. However, existing open-access vehicle logo datasets have many issues, which are hard to represent real-world conditions such as occlusions, low image resolutions and varying lighting conditions, thus heavily hindering the VLR application in reality. For this, a large-scale vehicle logo benchmark is proposed in this paper. Firstly, a large-scale vehicle logo dataset namely VLD 1.0 is collected, which contains 25,189 images of 66 classes. Each type of cars is collected with multiple images from various conditions to comprehensively represent real-world conditions. Secondly, a vehicle logo recognition evolution protocol is designed to fully evaluate performance, i.e., Mean Average Precision (MAP) under different Intersection Over Union (IOU) values and Speed. Thirdly, the YOLOv3 deep learning model is applied as a baseline method and its performance on the VLD 1.0 is reported.
Best Parameters Selection of Arrhythmia Classification Using Convolutional Neural Networks

Arrhythmia are disturbances in the heart where the heart beats slower or faster. Some types of Arrhythmia can became a serious problem and life-threatening. Early detection of Arrhythmia is very crucial to patients. Tools that can be used to determine heart condition is Electrocardiogram (ECG). Deep learning methods can be used to classify types of Arrhythmia from ECG images. Convolutional Neural Network is one of deep learning methods that is often used to classify images. CNN-based model such as VGG, ResNet, and MobileNet has gotten success in images classification. Those models are using lots of convolution layer, so those models are easily run into over fitting problem if those are used in small dataset. CNN model in this research needs parameter adjustments to solve over fitting problem. Parameter that were being adjusted were learning rate, dropout rate, and the number of convolution layer. The testing results on CNN model showed that the best learning rate and dropout rate which produced the best model to classify Arrhythmia were 0.0001, and 0.0075 respectively. The number of convolution layers which obtained the best accuracy was 4. Classification using CNN model for Arrhythmia with learning rate, dropout rate, and number of convolution layers were 0.0001, 0.0075, and 4 respectively resulted in the best model with 94.2 % accuracy value.
Measurement Scale of e-Complaint Service Quality

Emerging technology has changed human activities to be more efficient and effective. Citizens become more participatory in their involvement in the process of policymaking, decision making, or service design utilizing technology, communication, and information. In developing countries including Indonesia, electronic participation especially e-complaint has been recently implemented. There is a continuous desire from the citizen for better service quality along with the implementation. It is a challenge for the government to improve e-complaint service quality. The previous study claimed that we must first know how citizens apprehend and assess online to deliver better service quality. The objective of this study is conceptualizing, develop, refine the scale for measuring e-complaint service quality. The factor analysis with principal component analysis (PCA) used to stable the dimension of e-complaint service quality. We classified 23 item-scale under five main dimension of e-complaint service quality, they are technical efficiency, responsiveness, transparency, security, and citizen-support.
Automatic Design of Compliant Surgical Forceps With Adaptive Grasping Functions

In this paper, we present a novel method for achieving automatic design of compliant surgical forceps with adaptive grasping functions. Compliant forceps are much easier to assemble and sterilize than conventional rigid-joint forceps, hence their use is spreading from traditional open surgery to robot-assisted minimally invasive applications. However, many compliant forceps still perform stiff grasping, and thus can damage sensitive organs and tissues during the operation. Adaptive grasping function is therefore required for safe manipulation of vulnerable structures. Currently, it is difficult and time consuming to use empirical methods for designing adaptive compliant forceps for different surgical robotic applications. To cope with this problem, we developed a topology-optimization-based method able to synthesize adaptive compliant forceps automatically. Simulation and experimental tests were conducted to evaluate the adaptive grasping function of designed surgical forceps. The results demonstrated that the developed method greatly simplifies the design process and makes it possible to efficiently realize task-specific compliant forceps.
A Two Stage Convolutional Neural Network for Single Pixel Camera Image Reconstruction

A novel CNN architecture and non-iterative method to rapidly reconstruct signals from CS measurements for single pixel camera was proposed. This network reconstructed CS signals by dealing with different signal resolution scales step by step. The method divided the CS reconstruction into 2 stages. The first stage is the signal reconstruction at a low-resolution scale. The second stage is the upsampling procedure. At the same time, this study improved the loss function of the CNN model by adding a feature-based error. This loss function helped improve detail reconstruction of the signal. On a standard image set, the network showed significant improvement in performance, especially in time complexity. The network was more robust than traditional methods in noisy signal and low measurement-rate (MR) conditions. Even in extremely low MRs, the network could reconstruct an image very well. Furthermore, the network was tested with real data obtained from a single-pixel camera. The experiments showed that the network had better performance than other methods, and that it also exhibited robustness against sensor noise.
Area-Efficient Parallel Stochastic Computing with Shared Weighted Binary Generator

Stochastic computing (SC), a computing paradigm with strong fault tolerance, draws more attention recently. However, a drawback of SC is its long latency. One straightforward way to reduce the latency is to use parallel bit streams to encode data and apply multiple copies of the circuit to process the bit streams. However, this method multiplies the circuit area. In this work, we propose to share the random numbers sources (RNSs) and part of the probability conversion circuits (PCCs) to reduce the area of the parallel version of the stochastic circuit. In this work, we propose a method to share the weighted binary generator-based stochastic number generator to reduce the area of the parallel implementation of the stochastic circuit. Our experimental results showed that the accuracy of our design is slightly better than the traditional comparator-based design. Besides, our design can reduce the area-latency product by 58.5% compared to the serial implementation.
Measurement of Air Quality Index using Internet of Things

Air pollution is a colossal menace for the environment nowadays, requiring paramount interest. Air pollution can be controlled by monitoring the level of pollutant gases (NO<inf>2</inf>, NO<inf>x</inf>, SO<inf>2</inf>, CO<inf>2</inf>, CO, C<inf>6</inf>H<inf>6</inf>, O<inf>3</inf>, PM<inf>2.5</inf>, PM<inf>10</inf>, etc.) in a periodic manner. Sensing air quality is a major concern for environmental monitoring. In this paper, we are sensing the air from local environment by using generic air quality sensor with Scientech 6205 IoT builder. There are so many pollutants in air, out of which we are going to consider only PM<inf>2.5</inf> component for this work, as it is the most threatening compound among all the pollutants. The sensed data is then sent to Thinkspeak cloud platform. In cloud, the data is first processed and then analyzed for obtaining the value of air quality index. The generated results are then visualized by the graphical representation. On the basis of which, an individual can take precautionary measures for a healthy life.
Real-Time Sensor Fusion and Compensation for Wide-bandwidth Micro Vibration using Gyroscope and Camera

An algorithm for vibration compensation in long distance laser positioning applications on a vibrating vehicle is proposed in this paper. The vibration disturbance possesses multiple significant frequency allocating in wide bandwidth with 30 µrad amplitude. Such vibration is typical in moving platform based laser positioning and introduces significant displacement at destinations. In this paper, the low and high frequency domain vibration is sensed using the camera and gyroscope accordingly to eliminate any infrastructure requirement. A pyramid based Lucas and Kanade algorithm extracts feature displacement using the camera and a zero-phase recursive least square based filter is proposed to remove drift of integrated gyroscope data. A proportional and derivative controller fuse the processed data from both sensors. The resultant data is then proceeded to a piezoelectric driven Fast Steering Mirror system for compensation. A Sliding Mode Controller with Uncertainty and Disturbance Estimator is applied for actuation control. Experiments are conducted and the result show improved accuracy comparing to existing methods.
Novel optical approach for concentrating light in micro-CPV

Micro-concentrator photovoltaics (micro-CPV) is an evolution of conventional CPV, which has surged in recent years and has already received major international funding. Micro-CPV uses the downscaling of cells and optics to achieve benefits over conventional CPV technology. We present a solid single-stage concentrator optic, whose height and optical path would have been too large in conventional CPV sizes. Downscaling cell size to a few hundred microns reduces thickness and material volume of the optic proportionally, thus enabling high optical efficiency, high concentration ratio and low cost. A broadband optical efficiency of 81% can be obtained at a 1000X concentration ratio on a 100 µm solar cell including Fresnel losses and material absorption.
Acoustic Scene Classification Based on Additive Margin Softmax

In the acoustic scene classification task, different categories of scenes may contain the same acoustic events and the sound characteristics of the same category of scenes may also be significantly different, which results in poor classification performance. In order to solve this problem, a deep scene classification model based on convolutional neural network (CNN) is proposed with the additive margin softmax (AM-softmax) function as the measurement loss. The proposed model is able to acquire more categorical representative features of acoustic samples. The test results on the environmental sounds classification datasets ESC-10 and ESC- 50 show that the classification accuracy of the model is improved greatly compared with other methods based on deep learning, which achieve 94.75% and 80.25% respectively.