Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence Maximization on Hypergraphs via Similarity-based Diffusion

Influence maximization is an important problem in network science that aims to detect critical structures, such as nodes and interactions, with a higher influence on diffusion. It has applications in information spreading, rumor controlling, marketing, disease spreading, advertising, and more. Although the influence maximization problem in graphs has been studied ex-tensively, there are a few studies that explore critical structures in hypergraphs and these studies mostly focus on detecting influential nodes rather than higher-order interactions, i.e., hyperedges. In this paper, we study the influential hyperedge detection problem. We first design diffusion models on hypergraphs based on the similarity between hyperedges. Our claim here is that similarity between hyperedges is positively correlated with the diffusion process. To study this claim, we first calculate similarity scores between hyperedges and construct similarity-based hypergraph Laplacians. Next, we extend standard graph centrality measures for hyperedges using these Laplacians. We compare the similarity- based hypergraph Laplacians with the state-of-the-art influential hyperedge detection method using two evaluation metrics: the size of the giant component and the Susceptible-Infected-Recovered (SIR) simulation model. Our experimental results suggest that overall, similarity-based Laplacians are more effective than the state-of-the-art method in finding influential higher-order hyperedges.
Frequency Domain Feature Extraction and Long Short-Term Memory for Rolling Bearing Fault Diagnosis

With the rapid development of the high-speed rail-ways, the speed of trains is getting faster and faster, and the dynamic load between the wheels and rails of the vehicle increases accordingly. The rolling bearing is a key part of the high-speed train transmission system. The train is subjected to high-frequency vibration for a long time during operation, and the bearing is prone to fatigue damage, which affects the safe operation of the train. Nowadays, many methods have been applied in fault diagnosis like reinforcement learning, convolutional neural networks and autoencoders. One of the typical methods is the reinforcement neural architecture research method. It makes neural network design automatic and eliminates the bottleneck associated with choosing network architectural parameters. However, this method focuses on the time domain signal, and a time domain signal cannot capture the particular properties of a frequency domain signal. In order to solve these problems, we propose a new method containing two Steps: Use FFT to convert the time domain signal to the frequency domain and use Bi-LSTM neural network model to recognize different faults. For each fault, the time series signal has some correlation with some specific frequencies. The frequency domain is more intuitive than the time domain and describes different states of faulty types. For recognition, LSTM is better at classifying sequence data than other methods, and Bi-LSTM can predict the sequence from both directions, achieving higher accuracy. Experiments on public data sets demonstrate the efficiency of the proposed method.
Vision Based Grasp Type Selection in Hand Prosthesis Using Convolutional Neural Networks

Amputation can be due to several causes: traumas, blood vessel diseases, diabetes, blood clots, and so on. Amputation causes a few complications for amputees such as hand dominance, psychology, social challenges, endure host of discomfort etc. These amputees must rely on a hand prosthesis to move their hands and grip objects. Some are electrically powered, while others require mechanical movement to control the prosthesis using harnesses. Therefore, an automated vision-based grasp type selection controller for hand prostheses is suggested and designed in this study, mainly to aid the users and allow them to grasp an object easily without any effort. In this study, three pretrained Convolutional Neural Networks (VGG-16, Inception-V3, and DenseNet-201) are formulated through transfer learning to classify the types of grasps. The three grasp types classified in this study are palmar wrist pronated, palmar wrist neutral, and pinch. The architecture of pretrained models was tuned by replacing the three last layers with two dense/fully connected layers and a dropout/regularization layer. Regularization and dropout are two commonly used techniques to prevent overfitting. As a result, VGG-16 trained with both dropout and regularization layer achieves perfect 100% accuracy in classifying the objects to their respective classes, followed by DenseNet-201 and Inception-V3 trained with only regularization layer with 98.4% and 98.2% accuracy, respectively.
Automated Intracranial Haemorrhage Detection and Classification using Rider Optimization with Deep Learning Model

Intracranial haemorrhage (ICH) refers to a pathological disorder that requires quick decision-making and diagnosis. Computed tomography (CT) can be accurate and dependable diagnosis method for identifying haemorrhages. Automated recognition of ICH through CT scans with a computer-aided diagnosis (CAD) method will be useful to classify and detect the distinct grades of ICH. Due to the latest development of deep learning (DL) techniques in image processing applications, numerous medical imaging methods use it. Thus, this article develops an automated ICH detection and classification using Rider Optimization with Deep Learning (ICHDC-RODL) model. The presented ICHDC-RODL technique mainly determines the presence of ICH using DL concepts. In the presented ICHDCRODL technique, the features are generated by the use of Xtended Central Symmetric Local Binary Pattern (XCS-LBP) model. Moreover, the bidirectional long short-term memory (BiLSTM) method is employed for ICH diagnosis. At last, the rider optimization algorithm (ROA) is exploited for the hyperparameter tuning procedure of the BiLSTM method. To demonstrate the enhanced outcomes of the ICHDC-RODL technique, a series of simulations were performed and the results are examined under various aspects. The simulation outcomes indicate the enhancements of the ICHDC-RODL technique over recent approaches.
Fertilizer Spraying using Active Learning in Precision Agriculture to Improve the Crop Yield

The pests and crop detection play a major role in increasing the yield of crops, where pesticides and fertilizers help in prevention and control. The precision agriculture involving variable spraying technology is developing in recent past. However, most techniques are still in infancy level. Hence, it is essential in building a faster and efficient system in improving the spraying and plant protection mechanism. In this paper, a fertilizer spraying techniques involve with map matching algorithm using GPS is developed. In the study, a technique for map matching is proposed. (MMA) as an interactive system that enables reduced annotation cost and improved accuracy via active learning model. The models map the location points as a progression in the Hidden Markov Model (HMM) and generate automatically the results at initial iteration. The error prone points of spraying the pesticides are collected via trajectory and annotator is validated to reduce the error in finding the location points. The farmer can perform these operations either on online or in manual mode. The online model needs to set with the location details of the land to be sprayed and based on the GPS fitted in the vehicle, the fertilizer or pesticide are sprayed using the algorithm. Also, the decision of fertilizer or pesticide is selected employing a method known as map matching, which is based on the needs of the land.
Design and Development of a Mobile Application to Combat Digital Addiction and Dissociative States During Phone Usage

Design features such as “infinite scrolling” and the state which keeps users engaged in this behaviour can be described as a timelessness or dissociative state and has links to the onset of digital addiction. As addiction/dissociation and the effect of these design choices could therefore occur automatically for the user, an active prevention technique is needed to combat this behaviour. In this paper, we present analysis, design and development of a proof-of-concept solution for the interruption of dissociative of users. A mobile application has been designed and developed for an android device which incorporates the prior research and provides features relevant to the prevention of the problem area. The prototype allows to set up interruption time period and to select the applications to interrupt users on a session-based usage viewpoint. An evaluation has been made through useful and constructive feedback received from user testing. Through evaluation of the prototype solution, the application has potential use not only as a product for the general user, but as a tool for research into other methods of digital dissociative state interruptions.
Classification of Psoriasis Microarray Data using Machine Learning

Psoriasis is a common, chronic papulosquamous skin genetic condition that affects people of all ages. The most typical method is to identify the patient or particular skin lesion types. This research focuses on improving the performance of SVM and Naïve Bayes in the classification of psoriasis by performing parameter tuning based on kernel and, priors, and var_smoothing, respectively, using the Grid Search optimization algorithm. The goal of this research is to find the optimum SVM and Naïve Bayes parameters for classifying the psoriasis disease. This research consists of three phases. First focuses on literature review and investigation of current methodologies. The dataset used is psoriasis skin gene expression obtained from NCBI GEO Database. Second phase focus on the experimental design where the development of the SVM and Naïve Bayes classification model and the parameter tuning process using Grid Search algorithm were done. Lastly, in the third phase, the result obtained from second phase are observed, compared, and discussed. From the result of parameter tuning, SVM with kernel linear and sigmoid manage improve their performance from the preliminary run. Meanwhile Naïve Bayes default parameter are the optimum parameter setting in classifying the psoriasis disease and maintained its preliminary performance result. As conclusions, it is true that each parameter influences the performance of the classification model. At the end of the research, tuned SVM model with kernel sigmoid and its significant parameter were chosen as the suitable machine learning in classifying the psoriasis disease gene expression with 96% of accuracy and all the research objectives are achieved.
An application of Customer Embedding for Clustering

Effective and powerful strategic planning in a competitive business environment brings businesses to the fore. It is important for the growth of the business to move the customer to the center by acting more intelligently in the planning of marketing and sales activities. In order to find customer behavior patterns, the use of clustering models from machine learning algorithms can yield effective results. In this study, traditional customer clustering methods are enriched by using customer representations as features. To be able to achieve that, a natural language processing method, word embedding, is applied to customers. By using the powerful mechanism of word embedding methods, a customer space is created where the customers are represented based on the products they have bought. It is observed that appending customer embeddings for customer clustering have a positive effect and the results seem promising for further studies.
Data Mining Algorithm Data Model Data Analysis Based on Artificial Intelligence Technology

In recent years, as people use of information technology collection and analysis of data ability to improve and the rapid development of artificial intelligence technology, greatly promoted the data mining technology in the rapid rise of all kinds of basic scientific research, especially on the basis of artificial neural network intelligent algorithm of data mining technology is more widely used. This paper aims to study the data mining algorithm data model data analysis. First, this paper comprehensively expounds the basic theories and methods of data mining. On the basis of understanding and analyzing various data mining techniques, we focus on deeply analyzing and explaining the data mining technology based on BP neural network. Furthermore, this paper proposes a modified algorithm (HA-BP), which integrates multiple algorithm advantages and joint optimization, in view of the shortcomings of BP neural network algorithm, which is fully demonstrated in the nonlinear function simulation. Through the ideas and methods of data mining, it not only realizes the diagnosis and efficient prediction of abnormal data, but also realizes the effective value-added of basic data samples, and provides new research methods and ideas for mathematical analysis, influence factor analysis and even the main direction of subsequent research. The experiment has proved that the data extraction speed of the data mining algorithm is about 10 times faster than the traditional data mining algorithm, and the data mining accuracy has reached more than 95%. With the training and learning of the algorithm, the efficiency can be further improved.
A Methodology For Early Prediction and Classification of Heart Diseases in Diabetic Patients With Machine Learning Techniques

Heart diseases severely affects how people live. A serious condition that affects people of all ages worldwide. Diabetes and heart disease patients have significantly higher mortality rates. A lot of lives may be saved if heart disease could be predicted earlier, especially in people who have diabetes. Advanced machine learning algorithms assist in providing an earlier prediction and accurate method for classifying and predicting cardiac disease. In order to predict cardiac disease, this paper presents a thorough overview of both traditional and modern machine learning algorithms. Furthermore, it suggests a cardiovascular disease prediction model that is both efficient and accurate, notably for diabetes patients. This approach suggested uses the support vector machines and random forests. The experiments were performed, and the findings demonstrate how effective the suggested framework.
BRIMA Model: Innovation Shapes Radar Future

Glorious development of radar in the past century is actually a history of radar innovation, focusing on the continuous and innovative evolution in multiple dimensions such as requirements, technology, engineering and support to satisfy the increasing detection needs. From the perspective of organic thinking or system engineering, this paper comprehensively explores the universal laws and innovation-driving model in radar technology innovation, and puts forward RTES radar innovation ecosystem and a BRIMA radar technology innovation model. Finally, future technical development trends of radar are briefly described based on this BRIMA model.
Radar High Range Resolution Rrofile Attitude Estimation Based on Neural Network

The sensitivity of attitude is core problem of radar high range resolution profile (HRRP) target recognition, if the orientation of targets can be approximately confirmed, we can narrow the search area of template library in recognition. Then the real time of recognition results can be guaranteed, and the recognition performance can be improved too. In this paper, we propose a radar high range resolution profile attitude estimation method based on neural network, the data argumentation and regularization technique improve the generalization ability of model, and they make the model have some noise robustness.
Single-Phase Single Source Eleven Level Boost Inverter Topology

The aim of the proposed model is to reduce the number of switches which can boost the inverter voltage by using only one source and also achieve the self-voltage balancing on the single source-II levels. It is applicable to the inverter whose operating voltage is higher i.e., 440V meant for running the heavy motor (Power electronics) by employing the renewable energy source power generator. It improves the voltage by twice the input voltage without using any converters. The model uses the three capacitors, 14 switches and the single DC voltage source for 11 levels. The number of levels can be further extended to 17 by adding some switches and capacitors of topology. The proposed topology is being simulated in MATLAB/Simulink software.
Simulation of Flight Trajectories of Quadrotor using MATLAB and Simulink

The flight trajectories of quadcopter have been simulated in a three dimensional environment using MATLAB and Simulink. PID and PD controller is used for the pitch, roll and yaw controls of the quadcopter respectively. In the input stage, pitch, roll, yaw, North-East-Down coordinates have been applied to the quadcopter in the simulation environment. Different navigational waypoints have been used to generate flight trajectories, namely, triangular, quadrilateral and linear trajectories. No-fly zones are also included in the simulation environment.
Tracing Patterns in Electrophysiological Time Series Data

When multiple sensors record spatially proximate areas of activity, spreading activity patterns appear as temporally shifted signals in multiple time series. This is particularly prominent in the domains of medical and health analysis, where multi-sensory data is the object of time-elastic investigation. Tracing the spread of these patterns still remains a challenge in time series analysis. In this paper, we propose Motif Tracking for Spatially Ordered Time Series (MoTrack), an algorithm to efficiently track the propagation of individual patterns of activity throughout spatially ordered time series. Additionally, we present the concept of propagation trees to represent this propagation for a given point of origin. We investigate our proposal by applying MoTrack to high-frequency recordings of the electrical activity of β-cells located inside the pancreatic islet. The results confirm MoTrack’s capability to trace dynamically evolving signals in such recordings and indicate that future work using this approach can address current challenges in diabetes research.
A differential SAR Tomography inversion method based on Distributed Compressive Sensing

Over the last 20 years, SAR Tomography has emerged as one of the most powerful tools to map and monitor the 3D/4D structure of the earth surface, especially for complicated urban scene. Corresponding reconstruction algorithms have been evolving toward higher accuracy and finer resolution. Conventional spectrum estimation methods, i.e., plain Fourier transform, Capon filtering, MUSIC, SVD etc., suffer from limited baseline span and irregular spatial sampling. Whereas Compressive Sensing (CS) based methods i.e., standard CS method, Distributed Compressive Sensing (DCS) method, Multi-look Compressive Sensing method (MCS), can achieve much higher accuracy and super-resolution by exploiting sparsity or multi-looking. In a complicated urban scene, pixels of different characteristics correspond to different optimal CS-based methods. To achieve the best comprehensive reconstruction result for a whole scene, an adaptive selection of methods based on analysis of pixel scattering characteristics is proposed. Real data experiment is conducted on 31 TerraSAR-X/TanDEM-X strip mode images, and result comparison with the well-known SL1MMER algorithm has validated the proposed method.
A Three-Level Boost Converter With Peak Current Mode Control for Flying Capacitor Self-Balancing

This paper presents a method for balancing the flying capacitor voltage <tex>${(V_{CF})}$</tex> in a three-level boost converter. The proposed V<inf>CF</inf> calibration (VCFC) adjusts both charging and discharging periods of the flying capacitor to keep V<inf>CF</inf> constant at <tex>$V_{OUT}/2$</tex>. Thus, the proposed VCFC can eliminate the issue of V<inf>CF</inf> fluctuation caused by non-ideal effects, such as on-resistance mismatch of power transistors, timing mismatch of gate signals, leakage current, and parasitic capacitors in the three-level boost converter. The simulation results verified that V<inf>CF</inf> is calibrated to <tex>$V_{OUT}/2$</tex> automatically while achieving high efficiency of 93.4% for 10-to-30 <tex>$\mathbf{V}$</tex> conversion at load current of 100mA.
A Brief Analysis on Machine Learning Classifiers for Intrusion Detection to Enhance Network Security

Network-based applications present new threats and necessitate new security measures, which necessitate greater attention to speed and accuracy. The rapid expansion of malicious activity with the attacks poses substantial dangers to network security despite the numerous new security technologies that have been developed. Intrusion Detection Systems (IDS) are extensively relied upon by network managers to catch these kinds of network intrusions in the act. Detecting intrusions using machine learning is one of the most common ways, in which models are learned from data to distinguish between regular and anomalous traffic. Despite the prevalence of machine learning methodologies, there hasn't been much research into machine learning algorithms for intrusion detection. When it comes to protecting sensitive information and systems, the Network Intrusion Detection System (NIDS) is a must-have model. If a NIDS detects more threats than false alarms, it is considered to be a good tool for security. Intrusion detection systems that are able to adapt to the ever-changing nature of network threats are built using machine learning techniques. It's still unclear how effective and appropriate these machine learning approaches are for detecting advanced hostile attempts. In terms of accuracy, precision, recall, and training time cost, this study analyses the most common machine learning classifier approaches for intrusion detection. Developers can use this comparison to help them select the best NIDS development method. KDD99, CSE-CIC-IDS2018 and UNSW-NB15 datasets are used in the evaluation of the adopted basic machine learning classifiers. Classifiers like Decision Tree (DT), Random Forest, Support Vector Machine, Logistic Regression and K-Nearest Neighbour (KNN) are tested in the experiments. The classifiers working, limitations are briefly discussed in this manuscript.
UPAR Challenge: Pedestrian Attribute Recognition and Attribute-based Person Retrieval - Dataset, Design, and Results

In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r. t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic.