Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Nature, Cybercrime and Best Practices of Digital Footprints

Information and communication technology have accelerated the rate of change in society. As a result, digital identities have begun to form throughout this moment of transition. These identities are related to the acts people carry out in online settings. Through these actions, a digital footprint is created. However, many cybercrime attacks have occurred due to negligence of not properly understanding and taking care of digital footprints. This paper reviewed the importance and awareness of the digital footprints, the cybercrime involving digital footprints, and countermeasures associated with the digital footprints. First, the research is carried out by examining the nature of digital footprints to understand the operation of digital footprints. Then, cybercrime related to digital footprints is examined to comprehend their threats to digital footprints. Finally, the best practices to protect digital footprints are examined. Thus, understanding digital footprints will help users of all ages be more vigilant and attentive in taking care of their digital lifestyles.
Validation of the Role of Attention Mechanism in Predicting Brain Activity

In this study, we estimate the state of the human brain from visual stimuli by regressing the brain activity state from image features extracted from an image identification deep learning model. We introduce Attention Branch Network, which enhances to capture the features of the identified target by attention when extracting image features, into the image identification deep learning model and estimate the brain activity state from the image features weighted or unweighted by attention. Through experiments, we aim to verify the role of attention mechanism in estimating brain activity state from visual stimuli. As a result, we confirmed that the introduction of Attention did not have a significant effect on the estimation accuracy, but that differences were observed in the areas where the estimation accuracy was higher.
Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer’s disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.
Prediction of microsatellite instability of colorectal cancer using multi-scale pathological images based on deep learning

Immunotherapy is an excellent treatment option for many solid tumors, and the therapeutic effect has been proved in clinical. Microsatellite instability (MSI) has been an important predictive marker for response to immune checkpoint inhibitors for Colorectal cancer (CRC). CRC patients with high-level MSI can be provided immunotherapy and benefit from it. Some studies have attempted to predict MSI using pathological images based on deep learning, but the accuracy of the prediction model needs to be improved. In this study, considering that different scales of pathological images contain various levels of information, we propose a novel method, named MSIUMP, to predict microsatellite instability using multi-scale pathological images. We first predict the MSI of a patient based on deep learning model using different scale pathological images, and then integrate the results by ensemble learning to improve the generalization and robustness. In addition, a convolutional neural network model modified on the basis of EfficientNet is used to extract the information of patches from pathological images at different scales. Our method achieved the areas under the receiver operating characteristic curves (AUC) of 0.9096 in the internal test dataset TCGA-CRC and the AUC of 0.9619 in the external independent validation dataset PAIP2020. These results demonstrate the potential of our method as a prediction tool for microsatellite instability in colorectal cancer.
Cognitive load detection using Binary salp swarm algorithm for feature selection

Analyzing the brain's reaction to stimuli requires the detection of cognitive load during the mental assignment of neuronal activity. It is possible to determine the cognitive load experienced during mental arithmetic tasks using an electroencephalogram (EEG). Data from the mental arithmetic task (MAT) was gathered using EEG. The EEG signals were decomposed into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (CiSSA). After that, we extract the entropy-based features from IMFs. The Binary salp swarm algorithm (BSSA) was used to obtain the optimal feature subset for feature selection. Moreover, K-nearest neighbour (KNN) and support vector machine (SVM) machine learning models were used to classify a set of features based on performance indicators including accuracy (AC%), sensitivity (SE), specificity (SP), precision (PR), and F-Score (F-S). The best classification accuracy is achieved by the presented technique, which is 95.28% for KNN classifiers and 94.54% for SVM classifiers, respectively. The results of the study demonstrated that, in comparison to current techniques, the suggested approach is better accurate in detecting cognitive load.
Circularly Polarized Wide-Angle Beam Scanning Circular Array Antenna

In this paper, a left-handed circularly polarized wide-beam-width array element is proposed with an overlapped impedance and 3-dB axial ratio bandwidth from 1. 9GHz to 2. 2GHz. It consists of a circular microstrip patch with rectangular notched structure as the main radiator, a circular parasitic patch and a metal ring to broaden the impedance and 3-dB axial ratio bandwidth, and four metal posts to improve the beamwidth. A 12-element circular array is formed by using the aforementioned element, which can realize a wide beam scanning range from -72° to +72° in the elevation plane.
The Summer Heat of Cryptojacking Season: Detecting Cryptojacking using Heatmap and Fuzzy

Cryptojacking is a subset of cybercrime in which hackers use unauthorised devices (computers, smartphones, tablets, and even servers) to mine cryptocurrencies. Similar to many other forms of cybercrime, the objective of cryptojacking is achieve profit illegally. It is also designed to remain entirely concealed from the victim's view. However, its attacks continue to evolve and spread, and their number continues to rise. Therefore, it is essential to detect cryptojacking malware, as it poses a significant risk to users. However, in machine learning intelligence detection, an excessive number of insignificant features will diminish the detection's accuracy. For machine learning-based detection, it's important to find important features in a minimal amount of data. This study therefore proposes the Pearson correlation coefficient (PMCC), a measure of the linear relationship between all features. After that, this study employs the heatmap method to visualise the PMCC value as a colour version of heat. We utilised The Fuzzy Lattice Reasoning (FLR) classifier for classification algorithms in machine learning. This experiment utilised actual cryptojacking samples and achieved a 100 percent detection accuracy rate in simulation.
A Deep Learning Based Approach for Janya Raga Classification in Carnatic Music

Janya Ragas are Carnatic music ragas derived from the fundamental ragas called Melakarta Raga, by the permutation and combination of various ascending and descending swaras. Raga recognition is an important task, essential to research in Music Information Retrieval in Indian Classical Music. Even though the automatic raga recognition methods have been widely adopted in Carnatic music, most of the existing methods are restricted only to Melakarta Ragas. It is an important task to identify the Melakarta Raga of a Janya Raga, especially for improved music recommendation performance. In this study, we used deep learning-based approach to address this problem. Music files were collected for 65 Janya Ragas belonging to seven Melakarta Ragas. Classification-based supervised deep learning models: 1D CNN, LSTM and 1D CNN-LSTM were used in the study. The models were implemented to learn similarities from 1–20 mean values of Mel-Frequency Cepstral Coefficients (MFCCs) features of audio samples, by using TensorFlow and Keras APIs. The results revealed that the 1D CNN-LSTM model outperformed the other models with an accuracy of 82.0%. In the future, 1D CNN-LSTM based Siamese neural networks can be introduced to reduce the dependence on large amounts of labeled audio data.
A Low Cost EDA-based Stress Detection Using Machine Learning

Stress is an inevitable part of our lives in modern society since in many situations people are exposed to various stressors daily. According to studies, long-term stress can cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, stress detection is one of the crucial areas of study to maintain a healthy life. Recently, by developing commercial wearable technologies, real-time and continuous data collection for personal stress monitoring becomes more feasible. Under stress conditions, there are notable changes in physiological signals such as heart rate, respiration, perspiration, and eye pupil dilation. Previous studies have shown that Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), can identify stress. EDA measures changes in perspiration by detecting the changes in the electrical conductivity of the skin. This paper focuses on stress detection using only EDA wearable sensors and applied machine learning techniques. First, 87 different features are extracted from EDA signals. Then, the data are normalized per subject because of differences in individuals’ physiological responses. Finally, five dominant features in stress detection are selected. We used a publicly available dataset, namely, the wearable stress and affect detection dataset (WESAD) in this study. The results show that the One-Leave-Out method is capable of detecting stress with 97.03% accuracy.
Smoothed spectral abscissa tuning of an observer-predictor for input delay compensation of distributed time-delay systems

This manuscript presents an observer-based control for the compensation of the input delay in linear systems including pointwise and distributed delays, for which the smoothed spectral abscissa plays an important role in the tuning task. Specifically, tuning the state feedback gains of the observer-based control by optimizing the smoothed spectral abscissa, minimizes the $\mathcal{H}_{2}$ norm of the corresponding closed-loop system. Furthermore, tuning the observer gains by optimizing the smoothed spectral abscissa of the dynamic observer errors, ensures a fast convergence of the observer-based control to the future state. The proposed controller is applied to the stabilization of combustion in a rocket motor chamber.
Simulation Comparison of Electrical Characterization in PZT and PVDF on Copper (Cu) substrate based MEMS piezoelectric harvester

The aim of the work is to simulate and characterize lead zirconate titanate (PZT) on copper (Cu) substrate based MEMS piezoelectric energy harvester and compared with Polyvinylidene fluoride (PVDF). The two groups were PZT on Cu substrate and PVDF on Cu substrate.The simulation includes 20 samples each of two groups PVDF and PZT with 80% of pretest power and an error rate of 0.05. The samples represent average harvested power. Results: The average harvested power of PZT has the highest mean of 506.947 W (p ! 0.05) and PVDF has the lowest mean of 182.845 W with a significance value of 0.00 (p ! 0.05). PZT/copper based MEMS energy harvester has better efficiency than PVDF/Copper. e fluoride (PVDF); novel piezoelectric material; copper substrate; piezoelectric energy harvester; MEMS technologyr,
E-Band Multibeam Conformal Transmitarrays for Beyond 5G Wireless Networks

In this paper, an innovative methodology for wideband multibeam conformal transmitarrays with arbitrary curvatures is presented for E-band applications. A unit cell composed of three metal layers is designed to support wideband phase tunable transmissions. A full phase tuning range of 360° is obtained at 74 GHz with the transmission loss lower than 2.3 dB. By employing the proposed methodology, two conformal transmitarrays with different curvatures are designed for different platforms. Both prototypes have been fabricated and measured. Multiple beams are achieved with angular ranges of ±30° and ±45° in wide 3-dB gain bandwidths of 30% and 27.8% for the two prototypes, respectively. Both of them have covered the entire E-band.
Unsupervised Two-Stage TR-PCANet Deep Network For Unconstrained Ear Identification

The main aim of this paper is to present a novel speedy, lightweight, and efficient two-stage TR-PCANet model for features extraction. TR-PCANet uses PCA to learn the filters of the convolutional layers. In order to generate powerful filters, we propose to augment the data used for the training. The Filter Learning stage is followed by binary Hashing and Blockwise Histogramming stages. At the end of the network, we propose normalizing the histograms using Tied Rank normalization. Moreover, as it could positively affect the identification rates, we suggest reshaping all images using CNN as a preprocessing stage. To further enhance the recognition yields, we combine TR-PCANet with TR-ICANet and DCTNet. We conduct extensive experiments on the public AWE dataset. The obtained results have proven the efficiency of the proposed network against TR-ICANet and DCTNet as well as the relevant state-of-the-art methods including deep learning-based ones.
Towards A More Flexible Networking Landscape

Technological advancements leading to 5th generation networks mainly focused on improving coverage and performance in terms of bandwidth and latency. While these will likely remain aspects of continuous improvement, along with issues on reliability and security, this paper argues that flexibility is a key property that 6G networks should exhibit in order to overcome important limitations of the current networking landscape and fulfill emerging user needs and application requirements. We identify key areas that can contribute towards more flexibility, present existing efforts on relevant technologies and discuss the research challenges that need to be addressed in order to reach the desired level of flexibility.
What ionizes the interstellar hydrogen toward PSR 0950 + 08 and PSR 0823 + 26

Neither H II regions around nearby B stars nor known white dwarf stars can account for the free electron column densities along two well-defined line segments to the pulsars PSR 0950 + 08 and PSR 0823 + 26. The presence of the ionized gas seems to imply either (1) very long mean free path lengths for the absorption of Lyman continuum photons within the ISM (thereby suggesting a very different morphology for interstellar H I from that of the conventional view); or (2) Lyman continuum luminosities for early B or hot white dwarf stars that are more than an order of magnitude larger than currently accepted values; or (3) an additional and as yet unknown ionization source within the Galactic disk. 54 refs.
Research on Teaming Strategies Based on Network Science

Network science is an emerging field to study complex systems and physical laws. It is widely used in team cooperation, especially in analyzing sports teams. This paper constructs a passing network that determines the performance indicators of team cooperation and puts forward some effective strategies for the husky football team. In order to build a passing network, the first step is data processing, visualizing the Spatio-temporal data for better analysis and discovering the most frequent events, accounting for almost 80% of all events in football matches. Therefore, the link standard in the network is defined as the passing frequency when the node is a player. The more passes between players, the heavier the link. Based on the graph of gephi, the visualization network is established. The assessment of performance determinants is rather significant in sports sciences because the derived information is helpful for contribute to improving and expanding the coaching process. It is hard to select valuable indicators from the massive data, so this study employs the PCA algorithm for data reduction. After PCA processing, ten initial variables from datasets are reduced to 3 principal components while 81.53% of the original information is retained. Then, the three principal components are decided as performance indicators. Considering their practical meaning, we can conclude they represent coordination, positivity, and discipline in teamwork. In addition, to reflect the structural, configurational, and dynamical aspects of teamwork, we construct a Back Propagation Neural Network (BPNN) to predict the benefits of performance indicators, and the model shows high accuracy. In conclusion, a spatiotemporal passing network model and performance evaluation model based on PCA and BPNN are established to capture structural, configurational, and dynamical aspects of teamwork, and the models have high accuracy. Then, effective strategies are presented to improve team success. They are universally effective among sports teams and other teamwork.
Joint Spectrum Resource Allocation and Power Control for LTE- V2V Communication

LTE Vehicle-to-Vehicle (V2V) communication has emerged as a promising solution to meet the stringent requirements of automotive communication. In this paper, we propose a joint spectrum resource allocation and power control algorithm to maximize the system data rates of both Cellular User Equipment (CUE) and Vehicular User Equipment (VUE). Besides, the reliability requirements of CUEs and VUEs as well as the delay requirements of VUEs are guaranteed. Firstly, we propose a joint resource allocation optimal problem followed by transforming the optimal problem into two subproblems, i.e., the spectrum resource allocation subproblem and the power control subproblem. Secondly, we transform the spectrum resource allocation subproblem into a maximum weight matching problem for bipartite graphs. Then convex optimization and linear programming are applied to adjust the transmit power of users to further improve the system data rates. Finally, the effectiveness of the proposed algorithm is verified by the simulation results.
YouTube's Social Media Analytics as an Evaluation of Educational Teaching Videos

During the COVID-19 lockdowns in South Africa undergraduate laboratory sessions were forbidden, in turn, video-based tutorials were proposed as a tentative solution to address the lack of in-person practical demonstration sessions. Five videos were filmed on electrical engineering topics, uploaded, and then publicly shared on YouTube. An investigation was then conducted as to whether videos may be useful for the teaching of practical engineering content in the university context. This article is a report back on the findings of using YouTube as a platform for sharing and evaluating engineering educational practical tutorial videos. The gaol of this article is to introduce YouTube's social media analytics as a tool for educators to evaluate their educational videos. The findings suggest that educators may consider evaluating their videos using social media analytics, but these analytics should be reviewed critically and should comprise of several metrics measured temporally. Understanding YouTube's recommender system and its influence on the platform is also an important factor in evaluating one's video content.
Learning spatial structures and network correlations improves unknown protein–protein interaction prediction

The mechanism of action of protein-protein interactions(PPIs) is complex, and prediction models have to learn multiple dimensions to achieve excellent generalization performance. Therefore, based on the concept that the spatial structure of proteins is closely related to protein functions and the topological information of PPI networks reflects the correlation between proteins, we combine the protein structure information and the topological information of PPI networks to enhance the prediction performance. We present a new approach, SE3NET-PPI, for multi-type PPI prediction, retrieves protein structure information from the SE (3)-invariant matrix map generated by Alphafold2 and extracts the topological information of the PPI network using a graph neural network in the Siamese architecture. Results showed that our model outperforms several state-of-the-art methods under various dataset partitioning methods, with significant improvement in predicting invisible datasets. For example, in the case of Sring_al1-BFS, the miroc-F1 value of our model reaches 80.28 ± 0.43, compared to 75. 87± 0.37 for GNN-PPI and 62.30 ± 0.41 for PIPR. The implementation and related datasets are available at https://github.coml YY99117/SE3NET-PPI.
SCMA-LDPC coded communication system

Non-orthogonal multiple access (NOMA) methods are a promising technology that can be the basis of the next generation communication networks and ensure their high throughput. One of the most effective NOMA methods is sparse code multiple access (SCMA). SCMA allows providing better noise immunity and connecting a larger number of subscribers compared to classical solutions used in modern networks. Together with modulation and multiple access, it is reasonable to consider noise-resistant coding procedures that equally affect communication characteristics. One of the most efficient types of noise-resistant codes is low density parity check (LDPC) codes. Now LDPC is used in a number of communication systems, including 5G NR. In this paper, we propose the concept of a communication system with the joint use of LDPC and SCMA. We introduce a sequence of algorithms for the generation and processing of signals. The dependencies of bit error rate (BER) performance on SNR are given: they show the efficiency of the proposed solutions and the ability of the considered communication system to work in various conditions. The results obtained can be used to form a modulation and code scheme (MCS) table for next-generation communication systems.
Distance Measurement Method Using Neural Network Learning of Microwave Reflection Signals

In this paper, we present a novel non-contact distance measurement method using microwave reflection signals and artificial neural networks. Based on data learning, this method can effectively predict the distance of an object placed in a complex environment. In particular, by using a two-step neural network, we propose a method of maintaining precision while reducing the data used for training. Through an experimental test, microwave reflection signals for each distance are acquired and the two-step neural network is trained. Finally, the distance is estimated from the microwave reflection signal measured for an arbitrary distance. Using the proposed method, we have successfully demonstrated the distance measurement of an object placed in an underwater environment.