Miracle-dz/UniFAR-FTUs huggingface dataset:
LSTM-Based False Data Injection Attack Detection in Smart Grids

As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach.
Revitalizing Stock Predictions with Machine Learning Algorithms – An Empirical Study

The accurate prediction of stock prices has a significant role to play in the present economic world. However, stocks have an ever-changing nature coupled with a risk factor that always seems much of a gamble, making it worthwhile for researchers to analyze them with machine learning algorithms. We thus seek to develop a machine-based model replacing the traditional yet unreliable time-series forecasting to predict the stocks from the past historical data of a firm and to uncover the underlying patterns to improve the accuracy of the prediction of the model. As a beginner, we have decided to use a couple of conventional machine learning algorithms to study the behavior of learning techniques for stock prediction. This paper presents an empirical study to study and analyze the behavior of decision Tree, Linear Regression, K-Nearest Neighbours, and LSTM learning algorithms to bet on the algorithm that best predicts the stock prices.
Epoxy composite with hBN fillers compatible with industrial application in electrical equipment

Hexagonal Boron Nitride (hBN) has been used as secondary filler in an epoxy formulation in gas insulated substation (GIS) demonstrating higher electrical performance under HVAC stress, while being compatible with industrial process and constraints. A synergetic effect between hBN and alumina has been observed regarding thermal and electrical properties. A 500% increase in the thermal conductivity could be achieved with a limited impact on viscosity increase. Breakdown strength has been measured in flat sheets in oil and with embedded electrodes under divergent field simulating a defect. The breakdown strengths resulting from these different experimental setups show an interesting barrier effect from hBN even at low ratio in the formulation. Both results can be correlated with increased lifetime under thermoelectrical stress.
Towards Industrialization of FOPID Controllers: A Survey on Milestones of Fractional-Order Control and Pathways for Future Developments

The interest in fractional-order (FO) control can be traced back to the late nineteenth century. The growing tendency towards using fractional-order proportional-integral-derivative (FOPID) control has been fueled mainly by the fact that these controllers have additional “tuning knobs” that allow coherent adjustment of the dynamics of control systems. For instance, in certain cases, the capacity for additional frequency response shaping gives rise to the generation of control laws that lead to superior performance of control loops. These fractional-order control laws may allow fulfilling intricate control performance requirements that are otherwise not in the span of conventional integer-order control systems. However, there are underpinning points that are rarely addressed in the literature: (1) What are the particular advantages (in concrete figures) of FOPID controllers versus conventional, integer-order (IO) PID controllers in light of the complexities arising in the implementation of the former? (2) For real-time implementation of FOPID controllers, approximations are used that are indeed equivalent to high-order linear controllers. What, then, is the benefit of using FOPID controllers? Finally, (3) What advantages are to be had from having a near-ideal fractional-order behavior in control practice? In the present paper, we attempt to address these issues by reviewing a large portion of relevant publications in the fast-growing FO control literature, outline the milestones and drawbacks, and present future perspectives for industrialization of fractional-order control. Furthermore, we comment on FOPID controller tuning methods from the perspective of seeking globally optimal tuning parameter sets and how this approach can benefit designers of industrial FOPID control. We also review some CACSD (computer-aided control system design) software toolboxes used for the design and implementation of FOPID controllers. Finally, we draw conclusions and formulate suggestions for future research.
Sparse Blind Compressed Sensing Based on Bilinear Generalized Approximate Message Passing

By combining the bilinear nature of both the bilinear generalized approximate message passing (BiGAMP) methodology and the sparse blind compressed sensing (BCS) problem, a novel sparse BCS scheme is proposed. Based on BiGAMP, this scheme learns the sparse dictionary and sparse coefficients directly from the undersampled measurements, and then recovers the unknown signals by taking the standard compressed sensing as post-processing. Numerical investigation shows that this BiGAMP-based sparse BCS scheme performs better than the direct sparse BCS method, and is more adaptive to the signal under consideration than the standard CS with dictionary learning.
Adversarial Training Using Inter/Intra-Attention Architecture for Speech Enhancement Network

In this paper, we propose a new adversarial training for the end-to-end speech enhancement network. Taking the advantage of getting the paired training waveform, a new attention module is introduced into the proposed discriminator to incorporate the information of the desired waveform. Since this attention module has a role of the inter- and intra-attention mechanism, it helps the discriminator to distinctly distinguish the structural features underlying in the desired waveform and the waveform generated by the speech enhancement network. Unlike the other conditional generative adversarial networks, the proposed training architecture can simultaneously minimize the adversarial loss and the distortion loss. Through the simulation experiments for speech enhancement, we reveal that the proposed adversarial training can provide a significant performance.
Electrostatic Droplet Generator with 3-Coaxial-Nozzle Head for Microencapsulation of Living Cells in Hydrogel Covered by Synthetic Polymer Membranes

The current methods allow for encapsulation of cells inside spherical microcapsules made of a matrix covered by a permselective membrane using an electrostatic droplet generator with 1-nozzle or 2-nozzle heads. However, some potentially useful materials for the outer membranes cannot be put into direct contact with hydrophilic core filled by cells during the manufacturing process. Therefore, we designed a novel 3-coaxial-nozzle head that allows for the third fluid to separate the core material from the membrane material. The equipment was applied for manufacturing spherical microcapsules comprised of cell-friendly alginate core surrounded by semipermeable polyethersulfone membrane. The obtained microcapsules had a diameter between 0.84 mm and 1.79 mm, and the diameter correlated negatively with the applied electric voltage. The thickness of the membrane varied from 171 µm to 450 µm. The SEM images of the interior of microcapsules revealed highly porous membrane structure typical for synthetic membranes obtained by a wet phase inversion method. Bakery yeast cells encapsulated inside the alginate-polyethersulfone microcapsules retained their proliferation ability proving the effectiveness and safety of this encapsulation technique.
Identification of Biomarkers for Head and Neck Cancer by Analyzing Multiple mRNA Expression Datasets

Identifying biomarkers play a vital role in some clinical applications like diagnosis, prognosis, and drug selecting. Being the fifth common and frequent cancer in the world and having a poor survival rate, it makes Head and Neck squamous cell carcinoma (HNSCC) one of the most studied concern. Knowing the lack of effective and reliable biomarkers along with its high mortality rate, finding biomarkers from deferentially expressed genes can reduce the mortality rate. In our research paper, we studied different mRNA micro-array datasets (GSE6631 and GSE12452) for the identification of deferentially expressed genes. With the implementation of a parametric test (Student’s t-test) to identify the deferentially expressed genes with log fold change values. With careful inspection, we found a list of deferentially expressed genes consist of 365 up-regulated and 283 down-regulated genes in total by using Student t-test while keeping the p-value less than 0.05. Finally, after extensive analysis, two genes (PSPH and DST) were selected among them. With an overall endurance inspection, it indicates that PSPH and DST were associated with overall durability. Consequently, PSPH and DST can be the possible biomarkers and can act as a therapeutic objective to enhance the survival rate of Head and Neck Cancers. All the files and code which we used in this research paper are available at https://github.com/AmitHasanShuvo/BiomarkersHNSC
Evaluation of Feedback Method for FDD Massive MIMO in Device-to-device Communications

Massive MIMO is a key enabling technology for 5G. For efficient application of massive MIMO, frequency division duplexing (FDD) is considered as a better technique. But feed-back of channel state information (CSI) is a challenging task in FDD. This paper studies a conventional CSI feedback scheme and evaluate a cooperative precoder feedback scheme for CSI sharing via device-to-device (D2D) communication between users. This method is more efficient in minimizing interference and reducing delays. The fluctuation of sum rate for different transmission powers and carrier frequencies are measured. Numerical results provide sum rate against different sectors like additional blockage for 2nd user, total transmission power, number of bits required to feedback and delays for D2D CSI exchange. Simulation results illustrate that cooperative CSI feedback method outperforms all other feedback methods for FDD massive MIMO.
Molecular Geometries and Vibrational Spectra of Phenol, Benzaldehyde, and Salicylaldehyde: Experimental versus Quantum Chemical Data

Geometric and vibrational spectroscopic data (rotational constants, bond distances and angles, vibrational frequencies, IR intensities, and OH/OD isotope effects) of phenol, benzaldehyde, and salicylaldehyde as calculated at various levels of theory (HF/6-31G(d,p), HF/6-311++G(d,p), MP2/6-31G(d,p), B3P86/6-31G(d,p), BLYP/6-31G(d,p), B3LYP/6-31G(d,p), and B3LYP/6-311++G(d,p)) are reported. The theoretical results are discussed mainly in terms of comparisons with available experimental data. For geometric data (rotational constants and bond distances) the best agreement between theory and experiment is obtained at the MP2 and B3LYP levels. B3P86 calculated data are slightly worse, while HF and BLYP calculations yield distinctly too small and too large bond distances, respectively. B3LYP calculated vibrational spectroscopic data excellently agree with experimental IR spectra for phenol, phenol-OD, and benzaldehyde, and with minor restrictions, also for salicylaldehyde and salicylaldehyde-OD. Considering freq.
Multi-channel Speech Separation Using Deep Embedding With Multilayer Bootstrap Networks

Recently, deep clustering (DPCL) based speaker-independent speech separation has drawn much attention, since it needs little speaker prior information. However, it still has much room of improvement, particularly in reverberant environments. If the training and test environments mismatch which is a common case, the embedding vectors produced by DPCL may contain much noise and many small variations. To deal with the problem, we propose a variant of DPCL, named MDPCL, by applying a recent unsupervised deep learning method–multilayer bootstrap networks (MBN)–to further reduce the noise and small variations of the embedding vectors in an unsupervised way in the test stage, which fascinates k-means to produce a good result. MBN builds a gradually narrowed network from bottom-up via a stack of k-centroids clustering ensembles, where the k-centroids clusterings are trained independently by random sampling and one-nearest-neighbor optimization. To further improve the robustness of MDPCL in reverberant environments, we take spatial features as part of its input. Experimental results demonstrate the effectiveness of the proposed method.
A Nature Inspired Strategy for Demand Side Management in Residential Sector with Smart Grid Environment

A major portion of global electricity consumption is done by residential sector. The nature of load variation is highly uncertain and has become a challenge to control thereby contributes a lot in peak load burden on utility. However, the concept of demand side management (DSM) enables the utility to manage the residential sector load in smart grid environment. The concept of DSM in the smart grid enables the ability to facilitate the flow of information between customers and utility that helps to rebuild the curve. In this paper, human behavior-based algorithm is developed to alter the load curve by managing residential sector load. The proposed algorithm is tested with Python coding and offers satisfactory results. The considerable reduction in peak load with increased utility earning is noticed with critical peak pricing scheme (CPP).
Monthly Electricity Consumption Forecasting: A Step-Reduction Strategy and Autoencoder Neural Network

Accurate monthly electricity consumption forecasting (ECF) can help retailers enhance the profitability in deregulated electricity markets. Most current methods use monthly load data to perform monthly ECF, which usually produces large errors due to insufficient training samples. A few methods try to use fine-grained smart-meter data (e.g., hourly data) to increase training samples. However, such methods still exhibit low accuracy due to the increase in forecasting steps.
Determination of Interphase Thickness in Silicone Nanocomposites from Dielectric Properties

The material properties of polymeric nanocomposites can be modified significantly by the characteristics of the interfacial zone between the polymer and the nanoparticles. The present study analyzes the dielectric response in order to investigate the intermolecular interactions in the interphase at the boundary layer and to quantify them. For this purpose, crosslinked nanocomposites based on polydimethysiloxane and silica nanoparticles with different surface modifications (hydrophilic, hydrophobic) were produced. In the dielectric spectrum of the nanocomposites an additional relaxation is identified that is attributed to the interphase. The volume fraction of this interphase is determined from the dielectric decrement of its relaxation. Moreover, from three-dimensional transmission electron microscopy images, the three-dimensional distribution and the specific surface of the nanofiller is determined. By combining the specific surface with the result for the volume fraction of the interphase, the thickness of the interphase is calculated.
Placement of IPFC for Power Loss Reduction in Transmission lines using Firefly Algorithm

Firefly algorithm is proposed in this article for placement of Interline power flow controller (IPFC) to reduce power loss of a transmission line. The IPFC is one of the series FACTS devices. It can control active and reactive power of multiple transmission lines. Power loss reduction, improvement of efficiency of the transmission line and enhancement of voltage profile are major issues in the power transmission system. The average power loss in the system is taken as the objective function with various constraints. The code has been developed in MATLAB (Version 2019) and executed on personal computer. The proposed algorithms has been tested on IEEE 30 bus and NTPS 23 bus systems and the results are presented. It is noticed that the proposed algorithm provides best results in terms of power loss with less computational time.
Gender and Age Group Predictions from Speech Features using Multi-Layer Perceptron Model

Gender and Age estimation from speech is a challenging research area for voice technology domains for use in personal voice assistants, user-profiling, targeted marketing etc. In this paper we discuss the prediction of gender and age group from speech features using Multi-Layer Perceptron architecture. Features like Mel Frequency Cepstral Coefficients, formants, pitch, chroma values etc. are extracted from every speech sample and are then selected using Principal Component Analysis and Redundant Feature Elimination techniques. The paper explains 2 approaches named sequential model and single model approach for gender and age group prediction. The Common Voice repository from the Mozilla Speech Database is used for training and testing. The implemented model gave us an accuracy of 89.58%. We have explained the approaches used in existing solutions and their drawbacks, and proved that our approach is giving better accuracy than state of the art solutions.
Machine Learning Model for Sales Forecasting by Using XGBoost

For modern retail corporations operating a huge chain of businesses, exact sales predication is the key in driving corporations development, even success or failure. Sales forecasting allows corporations to efficiently allocate resources including cash flow, production, and make better informed business plan. In this paper, we propose an efficient and accurate sales forecasting model using machine learning. Initially, feature engineering is conducted for extracting features from historical sales data. Furthermore, we used eXtreme Gradient Boosting (XGBoost) to utilize these features for forecasting the future sales amount. The experiment results on a publicly Walmart retail goods dataset provide by Kaggle competition demonstrate our proposed model performs extremely well for sales prediction with less computing time and memory resources.
Solving Pediatric Vehicular Heatstroke with Efficient Multi-Cascaded Convolutional Networks

Pediatric Vehicular Heatstroke (PVH) is the situation where children suffer fatal injuries due to heatstroke after being forgotten in vehicles. It is a severe social problem: According to incomplete statistics, at least 864 children have died due to PVH since 1998 in the USA alone, and another 22 lost their lives in 2020. In this paper, we developed a machinelearning based embedded warning system that mitigates such tragedies. Specifically, we present our Children in Vehicles (CIV) dataset, where we collected 2,076 positive samples of children and 1,529 negative samples of empty car interiors. We then present the framework and training process of our multi-cascaded convolutional network architecture that can detect children with a 98% accuracy. Furthermore, we demonstrate the power of our novel curriculum learning method, which improved the classification accuracy of our facial age estimator from 46% to 62% and its F1 score from 0.66 to 0.91. We also deployed our complete pipeline onto an embedded platform to present its overall feasibility. Additionally, we open-sourced our code and dataset for others to use & experiment with.
Softwarized Optical Transport QoT in Production Optical Network: a Brownfield Validation

We compare measured QoT on production network to GNPy predictions, showing an excellent accuracy for 40 channels on two paths. Transmission exceeds 2000 km on mixed-fiber hybrid amplified links. Non-conservative prediction is observed only in 23% of cases and the inaccuracy is limited to 0.3 dB.
A Novel Approach to Coupled-Mode Analysis of Geometric Deformations in Reciprocal Waveguides

We present an approach to treat geometric perturbations in coupled-mode analysis for reciprocal waveguides. Geometric perturbations are converted into material ones and a coupled-mode theory for material perturbations is used to assess the propagation. The method is tested on an elliptically deformed antiresonant fiber.
Persian Emoji Prediction Using Deep Learning and Emoji Embedding

The appearance of social networks and the increasing expansion of these networks has created many challenges, especially in the field of natural language processing. One of these social networks that has been welcomed by many researchers is Twitter. Twitter’s users have the opportunity to consider one or more emojis for a tweet depending on the feeling and meaning of the tweet. Emojis contain information and concepts that the author of each tweet has in mind, the semantic and emotional range of each emoji is very wide and each emoji can be used in many different types of sentences. Therefore, by analyzing the content and emotion of each tweet, we can achieve the appropriate emoji of that tweet. For such reasons, predicting an emoji for a textual data is one of the challenges that has attracted the attention of researchers. In this article, using deep neural networks an attempt for the first time has been made to predict the emoji for Persian text data extracted from Twitter. And we were able to achieve F-score of 33% in 10 most frequent emojis which is 5% higher than the result of the SVM model and also 11% better than the result of the Naïve Bayes model, and F- score of 46% in 5 most frequent emojis which is 5% higher than the result of the SVM model and also 5% better than the result of the Naïve Bayes model.
Deep Analysis on Fisheries Classification

Classifying and detecting fishes in the wild is still an issue because of the various poses of fishes, ungovernable environment factors and the restricted quantity of data for training the model. The paper proposes an analysis of the techniques to classify different types of fishes caught on fishing boats without interference from humans using a Convolutional Neural Net (CNN). Different optimizers and different learning rates have been used and their findings have been evaluated in order to see how the two distinct CNN models work. To produce a comparative analysis of how one stacks against another in this particular problem, a dense CNN model, CNN 1 and a sparse CNN model, CNN 2 are built and analysed. The dataset used is publicly available on "The Nature Conservancy Fisheries Monitoring". Among the other models analysed with RMSprop and Adam optimizer, the dense CNN model with Stochastic Gradient Descent (SGD) optimizer offers the best outcome. A multi logarithmic score of 1.68087 is given.
Speech Emotion Detection Using Sliding Window Feature Extraction and ANN

Speech Emotion Detection (SED) aims to recognize the classification of each emotion in a continuous speech, and to pinpoint their onset and offset time. Although there is extensive research on emotion recognition, the research about detection is relatively less. In this paper, we propose a novel model for SED using the sliding window method for feature extraction and Artificial Neural Network (ANN) for modeling. As for feature extraction, we firstly introduce the window-level feature which is extracted by emotion recognition utilizing sliding window. Then, using Gaussian distribution to approximate the distribution of emotion contribution from interval-level to window-level in order to calculate the interval-level feature. Finally, ANN is used for model training which uses the fusion of the window-level feature and interval-level feature. We evaluate our model on EMO-DB and experimental results show that the proposed model outperforms baseline significantly.