Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Evaluation System for Pull-Up Exercises in Physical Fitness Testing Scenarios

With the cross-fusion development of artificial intelligence and computer vision in various fields, different industries are incorporating computer vision technology to achieve intelligence and automation. Especially in large-scale scenario tasks, unsupervised approaches can reduce labor and time costs. Physical fitness testing, as a necessary assessment subject for physical fitness, involves a large number of personnel and a relatively scarce number of professional instructors. This not only results in high manpower and time costs but also renders the assessment task meaningless without professional evaluation. In this context, this paper presents an intelligent analysis system based on computer vision that uses the pull-up exercise in physical fitness activities as an example. By utilizing the body pose coordinates feedback from pose estimation, combined with the proposed graph convolutional action evaluation network, the system can achieve segmentation of continuous actions and quality evaluation of segmented sub-actions. The test results demonstrate a high correlation between the proposed method’s evaluation and the results given by professionals. This system can also be applied to intelligent analysis tasks in other physical fitness exercises.
Multimodal Learning in Cyber-Physical System: A Deep Dive with WSCC 9-Bus System

Multimodal learning has been utilized for numerous applications in cyber-physical power systems for load forecasting, voltage control and power quality monitoring. Although this learning technique has attained prominence in recent years, the use of this technique for predicting physical anomaly for cyber disruption is rather limited. This study uses the knowledge of t-distributed stochastic neighbor embedding (t-SNE) as a method widely used in multimodal learning for predicting the state of operation of a cyber-physical system. Naturally the aim of a power system is to keep the system operating which is termed as the normal or stable operating state of the power system. In the event of a possible contingency, the cyber-physical power system should foresee such event and can take preventive measures considering the comprehensive knowledge pertinent to this study. The Western System Coordinating Council (WSCC) 9-bus power system is taken as the test case for this study. In the event of loss of a generator, the system is brought into stable operation by load shedding. In the meantime, the system also underwent a Denial of Service (DOS) attack which denied the load shedding command. As a result, there was physical impact in the system which caused the system to become unstable. This is mitigated by issuing firewalls, which ensures the load shedding command will be initiated. Fusing cyber-physical data from multimodal relays and sensors located at strategic locations of the power system, the analysis is performed. The fused data is used to give an accurate estimation of the operating state that is visualized with t-SNE.
Instantaneous Power based Effective Power Balance Operation in All-Electric Ship Microgrid

Shipboard microgrids suffer from frequently varying, bulkier propulsion loads and the existing operation of diesel engines leads to increased emissions and thereby operational cost. Also, the propulsion loads and other fluctuating loads produce unbalance and nonlinearity in the system thereby demanding load compensation to maintain power quality. Therefore, renewable sources and batteries are added to a hybrid shipboard microgrid (AC/DC network) to exert an uninterrupted power supply and to improve the power balance in the shipboard microgrid. The prime aim of this work is the usage of an interlinking converter capable of maintaining power balance in the system, bidirectional power transfer, and uninterrupted power supply through simple control methodology involving reference current generation based on instantaneous power theory. The instantaneous power theory involves the AC side source voltages and currents for the control and operates the interlinking converter at unity power during both PWM rectification and PWM inversion. The performance of the proposed control for an all-electric shipboard microgrid is validated in real-time through Software-In-Loop (SIL) approach by Opal 4510 Controller. The dynamic responses of the system through SIL are found to exhibit rectification at unity and preset power factors and also inversion at unity power factor with lesser total harmonic distortion.
Analysis of a Sensor Switching Approach for State Estimation with Applications to Electrochemical Power Converters and Energy Storage Systems

Equivalent circuit models for fuel cell stacks as well as for the series connection of battery cells are characterized by multiple series-connected RC sub-networks. In practical applications, for example, when aiming at the aging detection and monitoring of the individual cells in a fuel cell stack, it is desired to estimate the individual state variables of each of these series-connected cells. The same holds true for battery management systems that are dedicated to the state of charge (resp., voltage) equalization in a series connection of multiple battery cells. However, the pure knowledge of the current through this series connection as well as the sum over all terminal voltages is insufficient to make the overall system model fully observable. To efficiently observe the state variables in each of the series-connected subsystems, we aim at avoiding the measurement of the voltages of each individual fuel (resp., battery) cell. Instead, this paper presents a systematic sensor switching strategy which allows for estimating the individual state variables of all series-connected subsystems in an Unscented Kalman Filter framework. Simulation results are presented to compare the achievable estimation accuracy with a scenario in which all cell (resp., terminal) voltages were measured simultaneously.
On the Performance of Two-Way Full-Duplex RIS-Aided Vehicular Networks Over Double Nakagami-m Fading Channels

This paper investigates the outage and ergodic capacity performance of two-way (TW) full-duplex (FD) vehicular communication systems empowered with a reconfigurable intelligent surface (RIS). The bidirectional communication between two end user vehicles is established with the assistance of an RIS mounted vehicle having N passive reflective units. We deduce the novel tight analytical expressions for the overall outage probability (OOP), asymptotic outage probability and ergodic sum rate (ESR) by modeling vehicle-to-vehicle (V2V) links as double Nakagami$- m$ fading while considering residual self-interference (SI). The presented numerical and simulation results verify our derived analytical findings, and demonstrate the impact of different system and channel parameters on the system’s OOP and ESR performance.
Long Short-Term Memory (LSTM) Networks for Stock Market Prediction

The amazing capacity of Long Short-Term Memory (LSTM) networks to record complex temporal connections in sequential data has drawn a lot of attention in recent years. In the context of stock market prediction, a field where precise price movement forecasting is crucial, this study investigates the use of LSTM networks. We explore the basic ideas behind LSTM networks and explain how they may be used for time series forecasting because of their ability to analyze and understand sequential data. The first section of the study gives a brief introduction of LSTM architecture and its benefits over more conventional models. We talk about how important feature engineering and data preparation are to improving LSTMs' predictive power for stock market forecasting. We also explore how model assessment methods and hyperparameter adjustment may be used to maximize LSTM performance. In the study, we investigate several approaches, such as single-step and multi-step forecasting, for using LSTM networks in stock market prediction. We look at how external influences affect the performance of the model, including sentiment analysis of news and economic indicators. We also discuss potential remedies to the problems of overfitting and data scarcity that are inherent in LSTM-based stock market prediction.
Image super resolution using Enhanced Attention Block (EAB) for Grape Leaf Disease Detection

Accurate disease diagnosis in grape leaf analysis is heavily reliant on high-resolution (HR) images. To meet this need, we propose a model that incorporates the Enhanced Attention Block (EAB) to generate HR images for improved disease detection. The EAB employs advanced attention mechanisms and residual learning to effectively capture critical, spatial and channel-wise relationships. Proposed model is assessed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) metrics, yielding PSNR scores of 31. 902dB, 32.785dB, and 34. 853dB, and SSIM score of 0.8709, 0.9058, and 97.46 for factors 2,4 and 6 respectively. This proposed approach holds great potential for widespread implementation in disease detection across various plant species, allowing for timely and accurate interventions for plant health management and increasing industry productivity.
A Flexible Load Management Method for Digital Power Grid Based on Cluster Analysis

Flexible load, as an important schedulable resource, can greatly improve the balance of power network flow through refined control. Under the background of the current digital power grid, the Traditional mathematics mathematical model fitting method is not enough to achieve the analysis of nonlinear high-dimensional load data. In this regard, this article proposes a load forecasting model that combines an improved clustering algorithm with a short-term and short-term memory network. Firstly, for flexible loads, data processing is implemented using a multi core fuzzy C clustering algorithm, dynamically adjusting the training parameters of the model, and improving the feature description ability. Furthermore, the optimized long and short term memory network is used to train and learn the preprocessed sample dataset, capturing the bidirectional dependency information of the input data, effectively improving the predictive model’s ability to fit the characteristics of load sample data. The experimental results prove that the proposed flexible load forecasting model achieves precise fitting of the implementation situation and can achieve situational awareness and load control for the power grid.
Traffic Sign Board Recognition and Alert System Using R-CNN

Road accidents are a leading cause of fatalities, often resulting from the misinterpretation of traffic signs. Misinterpretation can occur due to driver negligence or reduced visibility caused by adverseweather conditions such as fog or heavy rain. To address this issue and prevent road accidents, we propose an innovative system that employs R-CNN for real-time traffic sign recognition. Our system operates by capturing a live feed from the vehicle's perspective and processing it in real-time. Using R- CNN, it identifies traffic sign boards and promptly notifies the driver via voice alerts. This system proves particularly valuable when visibility of traffic signs is significantly compromised due to adverse weather conditions. By recognizing and relaying crucial information, it empowers drivers to make informed decisions, thereby reducing the risk of accidents and enhancing road safety.
The development of geographical differences in education levels within the Swedish tourism industry

ABSTRACT The nexus of workforce and destination development is a largely uncharted area within tourism research. The local human capital has above all been in focus when underlining tourism's potential to create employment even for the inexperienced and less educated. However, a discordant strand of theory holds the low levels of qualified knowledge within the industry responsible for absent destination development in areas struggling to develop competitive advantages. Additionally, the in-migration of a skilled workforce may be hampered by a lack of amenities attracting permanent residents. In order to explore whether the tourism sector is characterized by a less educated share of the workforce, a study was performed using micro-level data on the full working population of Sweden during the years 2000–2010. The workforce in tourism was compared to the total and two other low-skill sectors, and the results show that the educational aspects within tourism are more related to geography than economic sectors. Contrary to general presumptions, the workforce within the tourism sector has a higher level of formal education than the other selected sectors in regions with generally low educational levels. The implication is thus that strategies aimed at creating employment for the least educated in rural areas need to be reconsidered, and not unconditionally target the tourism sector. Above all, tourism development needs to be based on assessments of local preconditions rather than a generalized image.
Exploring Electromagnetic Field Exposure in the Age of Non-Terrestrial Networks

The induction of non-terrestrial networks (NTNs) to support the operation of terrestrial networks (TNs) in underserved and remote areas has been gaining traction in standardization venues. The Third Generation Partnership Project (3GPP) has been discussing the introduction of NTNs to the fifth generation (5G) new radio (NR) since Release-15, where they conducted several studies to explore use-cases, network architectures, protocols, deployment scenarios, and possible challenges to the realization of NTNs in 5G and beyond. With these recent developments in technology, uncertainties concerning the levels of electromagnetic field (EMF) exposure to humans and the environments may arise. In this work, we highlight the aspects that are relevant in assessing EMF exposure while taking into consideration the implications of introducing NTNs to future communication networks.
Assessing The Impact of Bias in Training Data on the Fairness and Equity of Predictive Policing Models

In the evolving landscape of predictive policing, our project aims to unravel the intricate dynamics between training data bias and the overarching principles of fairness and equity. With the proliferation of data-driven methodologies, our study critically examines the extent to which biases inherent in training datasets can shape and influence the outcomes of predictive policing models. This paper critically examines the repercussions of bias present in training data on the equitable and fair deployment of predictive policing models. Through a comprehensive analysis, we investigate the complexities surrounding biased training datasets and their profound influence on the fairness and equity of predictive policing outcomes. The research delves into strategies for assessing and mitigating bias, encompassing conscious bias mitigation, diverse data collection, fairness-aware model training, and continuous monitoring. By emphasizing ethical considerations and community engagement, our study aims to contribute insights into crafting more equitable and just predictive policing practices.
MS-VAN3T-CARLA: An Open-Source Co-Simulation Framework for Cooperative Perception Evaluation

The CARLA simulator stands out amongst available open-source simulators providing life-like rendering of vehicular scenarios. While several frameworks, such as OpenCDA [1], have extended CARLA capabilities for the evaluation of cooperative perception applications, a significant gap remains in the use of accurate communication models. To address this need, we present an extension of the ms-van3t simulation framework, based on the ns-3 simulator. It integrates CARLA's high-fidelity sensor and vehicle physics, OpenCDA's data fusion and control automation, with ns-3's advanced communication models. Ms-van3t-CARLA offers a comprehensive tool for researchers working on the evaluation of the effects of vehicular cooperative perception under different communication technologies.
Data Quality IoT BoT Attack Detection using Progressive Learning Model

The Internet of Things’ development (IoT) devices in recent years has raised security concerns due to an increase in malicious traffic on a global scale. As smart devices based on IoT proliferate across several networks, so are the cybersecurity dangers associated with them. Big datasets like Bot-IoT, which train algorithms using machine learning on connections-based detection of intrusions for IoT devices, were created to help mitigate these dangers. Proper deployment of the Internet of Things (IoT) may bring about multiple advantages. It is a promising technology. For instance, Despite the fact that Internet of Things botnets have become a significant threat, there won’t be numerous full and meticulous studies evaluating the value of botnet detection methods in IoT applications. From these explanations, we developed a Conv1D-LSTM neural network for the predicting IoT BoT (Mirai, Gafgyt) attack. We also implemented a diverse. A variety of machine learning approaches involving as Logistic Regression, Random Forest and Support Vector Machine (SVM). Our analysis and experiments illustrate that our proposed neural network model can accurately classify the IoT BoT attack with 96.41%.
Performance Analysis of Deep Neural Network based Gamelan Musical Instruments Separation

The use of Javanese gamelan instruments or playing idioms in gamelan is widely used in various film compositions, video games, and contemporary music. Most gamelan instruments are made of metal, so separating the signals between instruments in evaluating gamelan music compositions is challenging. The U-Net architecture is used to test various methods and features; therefore, it can be done to conduct a comparative study to find the best neural network methods and features for Music Source Separation (MSS) in Javanese gamelan music. The neural network method tested was selected from methods often used for sequential data, such as Long Short-Term Memory (LSTM), Dual Path Recurrent Neural Networks (DPRNN), and Hybrid Demucs. There are 2 spectrogram input representations tested in the network, namely the magnitude spectrogram with wiener filtering (WF) and complex-as-channels (CAC). On average, the Hybrid Demuc's Source-To-Distortion Ratio (SDR) has the best value of 7.9 dB. In tests carried out using the evaluation of Source-To-Distortion Ratio (SDR), Source-to-Artifact Ratio (SAR), and Source-to-Interference Ratio (SIR), it is known that DPRNN is 31% superior using complex-as-channels (CAC) compared to WF. In comparison, LSTM is 20% superior using CAC compared to WF.
Comparison of environmentally sustainable 4-wheeler transportation in India using life-cycle greenhouse gas emissions

With its adverse effects, global warming has pushed for the penetration of alternate clean energy source vehicles to reduce the impact of GHG emissions from the transportation sector. In India, a major chunk of 4-wheeler sales happens in hatchback segments. So, this study has addressed a detailed life cycle emission analysis of a petrol ICEV, CNG, EV, and petrol-hybrid car to analyze the viability of these alternate clean energy fuel vehicles to replace the conventional petrol ICEV. The results showed that EV and petrol-hybrid in the hatchback segment showed equally lesser life-cycle emissions by 15% compared to petrol ICEV. Surprisingly, though the CO2 emission of CNG vehicles was found to be lesser than petrol ICEV by 7%, its total GHG emission showed the opposite result.
Automatic Face Recognition System Based on Data Augmentation and Transfer Learning

In an era marked by the ubiquity of digital identity verification, the realm of biometrics, especially facial recognition, has been a focal point of research. While it has exhibited impressive performance under controlled conditions, its efficacy degrades under uncontrolled environments. To address this challenge, we present a robust face recognition system inspired by Xception model. We harnessed the power of Data Augmentation and Transfer Learning techniques to proficiently train our proposed Deep CNN model on limited face datasets. These dual techniques not only led to heightened recognition accuracy but also served as robust safeguards against overfitting. Through rigorous testing using the Georgia Tech and ORL benchmark datasets, employing a two-fold cross-validation protocol, our system consistently surpasses contemporary state-of-the-art face recognition methods, achieving an outstanding 100% accuracy rate on both datasats.
Technical Debt Management: The Road Ahead for Successful Software Delivery

Technical Debt, considered by many to be the ‘silent killer’ of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to “bankrupt” those systems. Software engineers and organizations that develop software-intensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt; we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence.
Time Series Classification using Attention-Based LSTM and CNN

Time series classification has many real-world applications, including medical diagnosis, financial forecasting, and environmental monitoring. Accurately classifying time series data can provide valuable insights and help make informed decisions in various fields. This paper proposes the attention-based LSTM - CNN framework for classifying time series data. Our framework incorporates joint extraction of spatio-temporal features from a convolutional neural network and attention-based long- and short-term memory. The extracted features are fed into a Random Forest classifier to obtain the final output classes. We have used the Wafer dataset from the UCR repository for experimentation. An accuracy of 99.86% was achieved for the proposed model, hence transcending the performance of the other baseline models.
Improved iterative Poisson point cloud surface reconstruction

The traditional surface reconstruction methods include explicit and implicit ones, with Poisson reconstruction being a popular method due to its efficiency, simplicity and robustness. Uneven distribution of point clouds often leads to rough surface reconstruction results and holes. In view of this, an improved iterative Poisson surface reconstruction algorithm is proposed. This method uses Gauss formulas to reconstruct surfaces based on point cloud equipotential surfaces, utilizes principal component analysis to calculate point cloud normals, and then iteratively uses Poisson surface reconstruction algorithm. Furthermore, the algorithm uses indefinite width functions to calculate and update surface normals for more accurate results. Compared with the traditional Poisson reconstruction method, the improved iterative Poisson surface reconstruction algorithm has higher robustness and practicability in dealing with unevenly distributed point clouds, and can obtain better surface reconstruction effect with fewer iterations, with higher efficiency.