Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Machine Learning to Predict Mental Health Disorders and Interpret Feature Importance

The mental health and mental illness crisis has become increasingly acute in recent years, and many digital solutions with artificial intelligence at their core offer hope for reversing the deterioration of our mental health. Machine deep learning techniques can be used to analyse big data to build predictive models for psycho-education, assessment and screening to assess the mental health status of subjects, and can help the clinical community discover information that is not available to many traditional psychological research tools. This paper presents an in-depth analysis of a mental health survey and examines how it can be applied to the Al/ML domain of mental health research and how machine learning models can be used in this domain for fitting and prediction. Based on this, the importance of the presence or absence of current mental health disorders on other characteristics of respondents is assessed and visualised. It was found that the Cross Gradient Booster (Random Forest) model gave the best prediction fit among the various types of machine learning models, and the Grid Search algorithm was used to confirm that the final model had the highest accuracy of 0.79784 at a learning rate of 0.1. The Permutation Importance analysis revealed that the most important characteristic is whether or not the person has suffered from a mental health disorder in the past.
Possibilities of Adopting Electric Vehicles in the Agritourism Development Context

In a society that is looking for ways of sustainable development, the use of electric vehicles (EVs) can be an applicable solution in different fields of activity, including the tourism industry. The current research analyzes the possibility of using Buggy EVs in a predominantly agritourism region. In this sense, with the help of STELLA Architect, dynamic modeling software, a simulation is run to determine the potential behavior of tourists who use different types of off-road vehicles and their willingness to become Buggy EV users. Based on the data obtained, an algorithm is developed to size the fleet of Buggy EVs necessary for carrying out the activity, taking into account the most important characteristics of the existing Buggy EV, namely range, charging time and price. The results obtained from the study can be used as a starting point for improving the tourist experience, by integrating the technology we are so connected to.
Dual-layer Flexible EBG Structure for Broadband and High Gain Antenna Design

This paper presents a broadband, high gain antenna design consisting of a combination of flexible dual-layer electromagnetic band-gap (EBG) material and a conventional microstrip patch antenna. The frequency dependent reflection and transmission characteristics of the two EBG layers are used to enhance the impedance bandwidth and the realized gain of the antenna. Simulation results show that a 3 dB increase in antenna gain in-crease (from 7.35 dBi to 10.4 dBi) can be achieved with the flexible dual-layer EBG superstrate applied at target frequency of 3.4 GHz. A noticeable increase in impedance bandwidth can also be observed. Furthermore, simulations in bending conditions demonstrate that the performance of the proposed structure remains stable with the deformation and hence is suitable for application required flexible textiles. In cases where a compact design is desired, the proposed structure allows the antenna metal ground plane size to be reduced to 72% of its original size while maintaining a relatively stable gain, without necessarily requiring a large ground plane comparable to the EBG structure. Another advantage is that the dual-layer EBG structure can achieve an increase in both gain and bandwidth for single linearly polarized antennas. This study illustrates the performance enhancement and effectiveness of applying flexible EBG superstrated to form broadband and high gain antenna.
Evaluating the Impact of RPL Control Overhead on Network Performance

In IoT-LLN networks, RPL is a widely used routing protocol. RPL provides a lightweight and efficient way to route packets between devices in LLNs, creating a mesh network topology. For network bootstrapping and maintenance purposes, it periodically transmits DIO, DIS, DAO, and DAO-ACK packets. The lower-layer 6TiS CH protocol uses only a single minimal cell in a slotframe to transfer control messages. This increases the transmitting control messages' queue size. The packets in the transmission queue that have been waiting for a long time are removed from the queue. Network-associated nodes transfer control messages simultaneously, which causes the collision. Because of these issues, nodes waiting to join the network are not receiving the required control messages, and remain unassociated. Network-associated nodes must receive the DIO packets regularly to update the network topology information. If these nodes do not receive the DIO packets, it causes unnecessary parent-switching measures. Extensive simulations were done using Contiki-NG's COOJA simulator with varying network sizes, slotframe lengths, and hop sequences. Increased network size, slotframe lengths, and hop sequence caused more control message collisions, and parent-switching.
The CAADP Inaugural Biennial Review and the Africa Agricultural Transformation Scorecard: Results and Areas for Improvement

This paper uses the Biennial Review (BR) data and simple correlations to analyze the potential relationship between progress in recommitting to CAADP or mutual accountability and progress in meeting commitments in the other five broad areas. Various weighting systems are used to demonstrate the sensitivity of the weights chosen for computing the scores to develop the Africa Agricultural Transformation Scorecard (AATS). The current BR applies equal weights to the seven thematic areas, followed by equal weights to performance categories and indicators within each thematic area and performance category, respectively. The other weighting systems considered for the sensitivity analysis include equal weights applied at performance category or indicator level, differential weights based on the ease or difficulty in achieving various commitments using the Items Response Theory (IRT), and differential weighting system that gives more weight to performance categories or indicators that are more directly linked to agricultural transformation.
Research on Service Function Chain Orchestration for Intractable Scenarios

Service Function Chaining (SFC) has become a typical network service delivery approach in software-defined networking/ network function virtualization (SDN/NFV) network architectures. The complex network topology and dynamic nature of network resources in NFV networks have made the orchestration of service function chains a research hotspot and challenge. Existing studies consider mapping various types of virtual network function (VNF) nodes in the virtual service function chain to the underlying physical network, assuming that physical nodes can accommodate all types of VNF services, which imposes high demands on the performance of the physical network. In response to the aforementioned issues, this paper proposes a novel network architecture for scenarios where the physical network is intractable. To validate the effectiveness of the proposed network architecture, mathematical programming algorithms and genetic algorithms are employed to conduct experiments on the orchestration of SFC.
Induced Drought Tolerance by the Insecticide Imidacloprid in Plant

Imidacloprid is a systemic insecticide which has been used widely in various crops to control insects. In the present study, we demonstrated that pre-treatment of imidacloprid significantly induced tolerance to drought in plant. Relative water content, chlorophyll levels, and recovery rate upon rehydration after drought stress in tobacco plants pre-treated with imidacloprid were higher levels than the control plants. Induced drought tolerance by imidacloprid treatments in red pepper was also demonstrated by measurement of recovery rate and fresh weight upon drought stress. Taken together, our results suggest that imidacloprid, in addition to exerting direct insecticidal activity, may also protect plants by induced tolerance to drought in plant.
Research on key technologies of audiovisual media microservices and industry applications

With the continuous development of media convergence, in order to solve the problem that traditional technical architecture and business architecture are not suitable for new services, this paper proposes a media microservice architecture (MMA) and cloud-native microservice key technologies, and plans seven business domains suitable for audiovisual media. Through MMA, media organizations can improve deployment and iteration efficiency, reduce costs and increase efficiency, optimize media production processes, and expand converged media coverage. MMA has been applied in audiovisual organizations such as Hunan broadcasting system, iQiyi, and Mango TV in China.
Joint Angle Estimation of Lower Limb Based on PFA-LSTM-Attention Algorithm

In order to explore the applicability of surface EMG signal in continuous motion estimation of lower limbs, this paper proposes a method for optimizing weight allocation, introduces the attention mechanism in Long short-term memory networks, and optimizes the network model through Pathfinder algorithm to achieve continuous angle estimation of lower limb joints. Four motion modes were set up and eight characteristic muscles were selected to collect the original signals. After filtering and noise reduction, it is used as the input of the regression prediction model. The experimental results show that this method has high prediction accuracy and model stability in the angle prediction of multiple motion modes of the hip and knee joints, which provides conditions for the compliance control of the lower limb exoskeleton auxiliary clothing in the later stage.
Cyber Resilience and Smart Cities, a Scoping Review

The scoping review reported by this article aimed to analyze and synthesize state-of-the-art studies focused on the integration of cyber resilience in the implementation of smart cities. An electronic search was conducted, and 11 studies were included in this review after the selection process. According to the findings, cyber resilience represents a gap of the current research related to smart cities and, therefore, additional efforts are required to guarantee that smart cities are resilient to challenging events such as cyber-attacks or natural disasters.
Analysis of Electric Field Shielding Effectiveness of Composite Materials Based on Near Field Scanning

The electromagnetic compatibility (EMC) problem in high-speed integrated circuits has attracted much attention. Shielding materials have made great contributions to solving EMC problems. Shielding effectiveness is usually used to evaluate the shielding performance of shielding materials. In this paper, a special PCB testing fixture is designed, and a testing method of electric field shielding effectiveness based on near field scanning is proposed and utilized to measure the electric field shielding effectiveness (SE) of composite materials. Meanwhile, GTEM cell test method is also used to measure the SE of the composite materials. Finally, the permeability and conductivity of the composite materials are derived.
Quality of Leaves Classification Using CNN

This study demonstrates the incredible benefits of information technology (IT) to agriculture by demonstrating how well Convolutional Neural Networks (CNNs) with dropout can be used to classify the quality of rubber tree seedlings through their leaves. The researcher has gone through every fundamental step such as pre-processing, structuring of the model, training and evaluation of the model, and producing classification report. Data augmentation has been applied to increase the number of data sample during the training of CNN and it gives 96% accurate result for new images. It was noticed that during the evaluation of the model it posted 98% accuracy result in classifying the data with the same procedure set during the training the model. However, the classification report of each data sample that is not included in the datasets of training and evaluation.
IoT based on Smart Traffic Lights and Streetlight System

Energy wastage, traffic jams, and traffic monitoring are the three main problems the world's population is confronting. The traditional roadway management method is ineffective at controlling the flow of traffic when one lane has significantly more traffic than the others. Additionally, the present street light controller uses lights that are not turned off in the morning even though it is sufficiently light outside and that are turned on in the late afternoon before even the sun sets, which causes some power loss. This proposed system will offer an advanced smart traffic management system and intelligent street lighting system to bring a standardized approach to solving this issue. The enhanced smart traffic control system is accomplished by modifying the signal timings based on the comparison of the densities on all roads, i.e., when a specific lane has much more density compared to the other lanes, that specific lane is modified to a green signal for a specified length of time. By computerizing solar-powered street lights with IoT, energy wastage in the traditional street lighting system is prevented. The street lights are activated whenever an item moves, or they remain off otherwise. This proposed method reduces traffic issues, and energy wastage and improves the traffic monitoring process also.
A Lightweight on Spiking Generative Adversarial Networks for IoT Applications

The proposed algorithm describes SGAN (Spiking Generative Adversarial Networks) based on hardware for IoT Applications. One way to build an excellent dataset representation is to train Spiking Generative Advisory Networks (SGAN) and networks as sensory data for unsupervised tasks. Training various spiking MNIST datasets showed solid evidence that our spike confrontation pairs learn the repetition of the input data from the sensory data to the scene in both the spike generator and the spike discriminator. This algorithm improved a 20% speed up, 91.4% of accuracy and low power operation of IoT applications compared to other networks. For implementation, it was implemented through RTL coding and logic synthesis based on Xilinx FPGA. Logical synthesis results showed LUT 5%, FF 6%, IO 49%, and BUFG 3%.
Facial Emotion Detection using Machine Learning and Deep Learning Algorithms

Emotion recognition plays a critical role in various domains, such as human-computer interaction, psychology, and market research. With the growing popularity of social media platforms and the increasing use of multimedia content, the ability to automatically recognize emotions from images has become a significant research area. In this research, through a pre-trained CNN model, rich and high-level features that effectively capture the emotional content present in the images are extracted. The dataset for the same is FER2013 which is downloaded through Kaggle and the data to test emotion will be taken live feed through a webcam. The extracted features are then fed into three classifiers namely: CNN, KNN, and random forest. Conducted experiments on a publicly available emotion recognition dataset named FER-2013. It illustrates the efficacy of change in accuracy by achieving a vast difference between the CNN, KNN, and Random Forest. Moreover, compared the performance with other state-of-the-art methods, demonstrating its superior performance in accurately recognizing emotions from images.
Improving the Process of Gamified Systems Development by Emphasising Usability

Gamification of various processes is gaining recognition as an effective means of increasing motivation, engagement, and satisfaction of people involved in activities not associated with games, such as education, healthcare, or work. Gamified processes do not have to be computerized, but if they are, the software systems used during them typically share a host of specific characteristics putting them on a separate shelf, typically as a subtype of information systems. This creates the demand for specialised development methodologies that would merge traditional software life cycle with gamification processes. At the same time, it is known that the level of user participation, which is highly critical for gamified systems, depends significantly on the usability of the system. In this paper, we present a generalized gamified systems development methodology and its extended version that emphasizes high usability of the final product. To achieve it, actions must be taken both during the requirements specification, when the game components and usability requirements can be chosen based on the parameters of the given gamification project, and during evaluation of intermediate or final results of the development.
Modeling and Simulation of Electric Vehicle Driveline

Electric vehicle drivetrain modeling and simulation is a crucial aspect of electric vehicle design and development. A driveline is responsible for transferring power from the motor to the wheels, and the modeling and simulation process helps optimize the drivetrain design for efficiency, performance and safety. This paper presents in an extended manner the design and mathematical modeling of a dynamic transmission system of an electric vehicle. The mathematical model was simulated in Matlab Simulink and the obtained results were the basis of the implementation of the entire system in a small vehicle with offroad capabilities. This work is part of a larger project that has as its objective the creation of a complete electric vehicle starting from the design part and up to the testing on the roads.
Research on the Prediction of Artificial Intelligence Enterprise Value Based on Improved PSO and BP Neural Network

An important prerequisite for promoting the healthy development of the artificial intelligence industry is to make a more scientific and accurate prediction of the value of artificial intelligence enterprises, so as to better realize the effective allocation of resources in the investment market. In view of the wide application of BP neural network in the prediction of nonlinear fields, this paper uses particle swarm optimization algorithm and BP neural network algorithm to construct an artificial intelligence enterprise value prediction model based on improved particle swarm optimization algorithm and BP neural network, and uses Matlab tools to analyze and predict the value evaluation indicators of artificial intelligence enterprises. This study selects the daily stock trading data of IFLYTEK, a leading enterprise in the artificial intelligence industry, from 2020 to 2023. The results showed that the RMSE, MAE and PR of BP neural network were 1.275, 0.724 and 98.690%, and the RMSE, MAE and PR of PSO-BP neural network were 0.586, 0.389 and 99.246%. It shows that compared with BP neural network prediction model, the relative error of actual data of PSO-BP prediction model is smaller and the accuracy is higher, which can provide an effective model for the subsequent artificial intelligence enterprise value evaluation.
EPA ECC: Error-Pattern-Aligned ECC for HBM2E

DRAM vendors introduced On-Die Error Correction Codes (OD-ECC) to correct errors internally. Most OD-ECCs are based on Single Error Correction to correct individual bit errors. However, recent soft error experiments on HBM2 reveal that DRAM frequently experiences multi-bit errors, necessitating a stronger OD-ECC solution. This paper introduces a novel OD-ECC, EPA ECC, specifically designed to correct frequently-observed multi-bit error patterns. The key innovation of EPA ECC is the construction of multi-bit symbols aligned with common error patterns, and the application of Reed-Solomon codes to correct severe errors without increasing the redundancy ratio. Our evaluation demonstrates that EPA ECC provides higher memory reliability than the current SEC-DED OD-ECC without incurring significant system performance degradation.
Spatio-Temporal Wind Speed Prediction Based on CNN-GRU

With the booming development of China's ocean shipping and tourism business, the demand for safety and economy when ships are sailing at sea is increasing. Accurate meteorological and hydrographic forecasts can provide meteorological navigation for ships to avoid typhoons and bad weather areas as much as possible and reduce the damage to the ship's hull from wind and waves. To obtain accurate and reliable wind speed prediction results, this paper combines the advantages of convolutional neural network and gated recurrent unit network to form a deep convolutional gated recurrent unit network model (CNN-GRU). For multiple locations, the CNN-GRU algorithm is used to extract the characteristic meteorological elements, and then the convolutional neural network is used to establish the spatial characteristic relationship between each location, and the gated recurrent unit network is used to establish the temporal characteristic relationship between historical time points, and the final wind speed prediction results are obtained based on the spatio-temporal correlation analysis. In this paper, the CNN-GRU model was established using meteorological data from 2019 to 2021, and the prediction results were compared with CNN model and GRU model and the accuracy was verified. The results show that the experimental results obtained by the CNN-GRU model are more accurate and prove the effectiveness of the proposed method.
Leveraging Corporate Engagement for Diversity in Free/Libre and Open Source Software Projects

In this workshop paper, we describe results from a mixed-methods study of social diversity and corporate engagement in free/libre and open source software (FLOSS) projects. We collected survey responses from contributors to FLOSS development on GitHub to characterize perceptions of social diversity and corporate involvement in projects. We additionally analyzed data extracted from FLOSS projects hosted on GitHub to investigate differences in diversity based on corporate engagement levels. Our results suggest that organizational decisions may be detrimental to both the expansion of a project’s contributor base and for increasing diversity across FLOSS ecosystems. However, as some survey participants note in their responses, organizations subsidizing FLOSS development have opportunities to increase access to and openness of projects which would be beneficial for diversity. This research thus serves to identify organizational factors and actions which harm and help initiatives to improve inclusivity and equity in FLOSS development.
Autoencoder for Single-pixel imaging

Single-pixel imaging (SPI) as a new type of computing imaging technology, has important application value in the fields of biomedical images, long-distance imaging, remote sensing and so on. Limited by imaging mechanisms, high-spatial resolution single pixel imaging is inefficient. Obtaining high-quality imaging at low sampling rates is a hot and challenging issue in the field of single pixel imaging. In recent years, artificial intelligence methods represented by deep learning have been widely used in single pixel imaging. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. However, how to get sufficient training sets in many practical applications, e.g., long-range targets single-pixel imaging is very difficult. Here, we introduce and motivate a new training principle for unsupervised learning of autoencoder (AE) based on the single pixel imaging physical model. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. Comparative experiments clearly show the surprising advantage of AE in low sampling rate of time-varying modulation patterns.