Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Emotion Recognition from EEG Signal Utilizing Wavelet Packet Node Reconstruction and a CNN Classifier

Emotion recognition using electroencephalogram (EEG) has encompassed a broad research area in the domain of affective computing since the change of emotional states in response to variations in brain activities. In this paper, a novel emotion recognition approach is proposed employing the wavelet decomposition and node reconstruction of EEG signals. Following the wavelet decomposition of the EEG signal into five different levels, a node reconstruction scheme is employed to reconstruct the signal at each level to retain the original signal length. Subsequently, a 2D matrix is formed by combining the reconstructed signal information at all decomposition levels. In view of incorporating the information of multi-channel EEG signal, a 3D frame is formed combining the 2D matrices acquired from all available channels. The frame is applied as an input to a 2D deep neural network where the signal information at different decomposition levels is passed to the depth dimension of the neural network. The proposed method offers substantial performance in the classification process for the valence and arousal domain considering the performance of all subjects. The average accuracies obtained for valence and arousal domains in binary class problems are 93.06% and 91.93% respectively.
Exploratory Review: Trust Dynamics in AI-Enabled Retail Financial Investment Service

Trust anchors financial markets, which directly contribute to the foundation of global economies, and simultaneously fuel FinTech innovations. AI-driven tools such as Robo-advisors, Equity crowdfunding, and Peer-to-peer lending reshape investment paradigms, but understanding trust within this digital realm remains elusive. This scoping review examines AI trust dynamics within retail financial investments. We locate and dissect thematic constructs, and assess the complex interplay of pivotal variables. While initial insights emphasize trust’s critical role in FinTech’s evolution, they also illuminate the constraints of a universal framework to analyze trust in this context. Despite the popularity of models like TAM and UTAUT, their inherent weaknesses leave important facets unexplored. While qualitative and quantitative methods predominantly inform the current discourse, many studies base their conclusions on niche or self-crafted hypotheses. Through this systematic review, we chart a path for future discussions on AI-driven financial trust, highlighting gaps, and offering new avenues for exploration.
Smart Contextual Modeling for Customer Data Interchange

Smart Contextual Models (SCM) are psychologically based abstractions that show the customer's systematized task execution. System designers build on existing frameworks and knowledge by developing interfaces and applications that mimic contextual models, which makes it simpler for users to learn how to use the new artifacts. One such model that we would like to present is the simplest approach to convey the necessary information to the Field Service Professionals (FSPs) to develop a continuous engagement with corporate resources, and it should be as natural sounding as discussions between two people. To maximize the effectiveness of handling organizational data and improve the customer experience, we present smart contextual modeling in this paper. In this case, data exchange essentially allows data sharing across several FSPs and offers access to data segments to easily get in-depth knowledge to address the issue at the customer's location. It will raise customer happiness and service staff satisfaction, which suggests a move toward next-generation computing. Two separate real-time studies are used to assess the proposed SCM approach to prove how well it handles the interchange of video data.
Predicting Brain Tumor Region from MRI FLAIR Images using Ensemble Method

The conventional approach to tumor detection primarily hinges on the expert evaluation of physicians, a process susceptible to time constraints and inherent inaccuracies, thereby posing imminent threats to patients’ well-being. However, the integration of the Fluid Attenuated Inversion Recovery (FLAIR) modality offers a transformative avenue. It empowers medical practitioners to gain profound insights into the extent of tumor infiltration, representing a significant advancement in diagnostic capabilities and potential treatment strategies. This study delves into the realm of brain tumor assessment and prediction using various deep learning CNN architectures like ResNet50 U-Net, EfficientNetB3 U-Net, and InceptionV3 U-Net. The U-Net architecture, in particular, stands out for its adaptability, allowing the incorporation of several neural network pre-trained models within a single framework. The investigation of ensemble modeling resulted from a focus on improving the effectiveness of the segmentation approach. This method makes use of the previously described models’ combined advantages, enabled using an average of softmax. This cooperative endeavor resulted in the Ensemble model’s development, which surpasses its individual analogs in terms of performance measures. Combining multiple models with their unique strengths and weaknesses enhances overall ensemble robustness. Diverse segmentation models employing different features benefit from ensembling, capturing a wider range of patterns for improved performance. In this context, the ensemble model emerges as the most effective in terms of segmentation performance, achieving a Dice Similarity Coefficient of 93.29% and an Intersection of Union score of 87.65% on the datasets. These metrics quantify the overlap between actual and predicted regions, showcasing the superiority of the ensemble model which outperforms the state-of-the-art methods.
Achieving Privacy-Preserving Outsourced SVM Training with Non-Linear Kernel

Cloud-based Support Vector Machine (SVM) is a powerful technique for decision-assistance service. However, training data and models of SVM contain sensitive information, outsourcing these data to clouds may lead to severe privacy leakage. To address the privacy issue of SVM, many works focus on outsourced privacy-preserving SVM training. However, these works cannot support training SVM with non-linear kernel. This limitation renders these methods impractical for real-world scenarios where datasets are usually non-linear. In this paper, we propose a privacy-preserving SVM training scheme with support to non-linear kernel. Specifically, we redesign a gradient descent for SVM with kernel, which supports efficient kernel SVM training. We design basic computation protocols using secret sharing to achieve privacy preservation in outsourced SVM training. Additionally, we construct an incremental learning approach to support continuous data inflow. This approach is capable of reducing computational overhead significantly in practical scenario. Security analysis and efficiency evaluation illustrate that our proposed scheme achieves superior accuracy and less computation overhead compared to existing works, while also preserving privacy of training data and trained SVM model.
Reinforcement Learning-Based Security Orchestration for 5G-V2X Network Slicing at Cross-Borders

As part of the 5G, Connected and Automated Vehicles (CAVs) will benefit from Network Slicing (NS) in several tailored 5G- Vehicle-to-Everything (V2X) services running on the same physical infrastructure. However, the use of 5G- NS may also increase the risk of cyber-attacks that could compromise 5G-V2X network slices (5G-V2X-NSs) and cause significant harm to CAV's passengers. This risk is particularly high at cross-borders, where CAVs move from their Home Mobile Network Operator (H-MNO) to a Visited MNO (V-MNO), with similar 5G-V2X-NSs in place. Therefore, deploying security services to neutralize 5G- V2X NS threats in this scenario is mandatory. However, if H-MNO and V-MNO act independently, deploying these security services could be inefficient and may result in increased memory, processing, and network resource consumption. Thus, MNOs should collaborate to orchestrate their security services to neutralize 5G-V2X NS attacks and optimize their costs efficiently. In this context, this paper proposes a novel approach to enhance the security of 5G-V2X NS at cross-borders using Reinforcement Learning (RL) based security orchestration. Specifically, we trained and deployed an RL agent interacting with both H-MNO and V-MNO. The RL agent efficiently deploys security services to effectively remove threats, optimize resource utilization, and minimize the impact on 5G-V2X-NSs. The performance results show that the RL-based security orchestration neutralizes threats with an average success rate of almost 100%. Additionally, resource consumption is minimal at less than 8 %, and the acceptable impact on 5G- V2X - NSs is negligible, averaging less than 12 %.
A Seafood Detection Method Based on Improved YOLOv5s

In response to the problem of low accuracy in underwater target detection and recognition, this article establishes a dataset of marine products in complex underwater environments and proposes an underwater target detection model based on improved YOLOv5s. The model uses VariFocal Loss to calculate classification loss and solve the problem of imbalanced positive and negative samples. The bottom layer of the model structure is constructed using a cross scale feature fusion layer to obtain a larger receptive field, and a global attention mechanism module is introduced into the YOLOv5s backbone network, Enhance feature extraction, enhance the ability to collect features, and improve the accuracy of underwater target detection. The test results show that the improved model achieves an average accuracy of 71.5% for underwater target recognition, which is 6.3 percentage points and 3.7 percentage points higher than the YOLOv5s and YOLOv51 algorithms, respectively, making it more suitable for underwater target detection tasks.
Study on the Environment State Perception Technology of the Finite Space

Environmental states in a limited space mainly include: gas composition, gas concentration, ambient temperature, ambient humidity and ambient wind speed, etc. Environmental state perception mode mainly monitors and analyzes environmental states in a limited space area or restricted part, and has the functions of data analysis and storage. This paper further improves the emergency protection measures for limited space operations, realizes full-time perception, early warning, quick warning, and emergency evacuation, and reduces the risk of blind rescue and stray into dangerous areas, in order to avoid the escalation of the scope and impact of safety accidents, effectively improve the emergency protection capability of power grid limited space operations, and the IoT and intelligent level of emergency protection technology for power grid equipment and facilities.
Watermarking Based Waveform Synthesis in Joint Communications and Sensing

In joint communications and sensing (JCS), which is expected to be a prominent feature of 6G wireless networks, the major challenge is how to integrate both functions of communications and sensing in the same waveform. In this paper, a watermarking-based scheme is proposed, where the communication message is considered as watermark, hidden in the host waveform that is dedicatedly designed for radar sensing. Moreover, the watermarking is based on dithered quantization, which is optimized in order to minimize the distortion to the original host waveform. The mitigation of interference in JCS networks is also discussed. Numerical simulations are carried out to demonstrate the proposed watermarking JCS scheme.
Assisting Independent Travel for Blind People Using Smartphone-Based Object Detection

Access to environmental information, such as street signs, is crucial for blind people to travel independently. Recent advances in computer vision and machine learning techniques have opened up new opportunities to aid blind travelers by informing them of the presence of potentially significant objects in their surroundings. In this paper, we focus on the feasibility of leveraging such techniques to assist blind people in two travel tasks: operating vending machines and accessing barrier-free restrooms in public spaces. We implemented two smartphone application prototypes that provide voice guidance to aid blind travelers in performing these tasks. Specifically, one prototype helps them detect relevant objects for a vending machine (e.g., products and coin acceptors), while the other prototype helps them detect a restroom facilities (e.g., toilets and handrails). We conducted a user study on the use of our prototypes to perform these tasks with eight blindfolded participants. The evaluation results from the study demonstrate the applicability of our approach in enhancing the environmental awareness of blind travelers.
A Study on Automatic Open-Domain Dialogue Evaluation Metrics

Given the extensive use of conversational systems in daily life, there arises a need for an automated evaluation system tailored to such conversations. In light of this, an array of studies is underway to develop automatic evaluation metrics. Unlike conventional approaches, contemporary methodologies incorporate techniques that leverage the contextual nuances of conversation systems. In conclusion, it points out that recently explored methodologies are better suited for assessing conversations compared to classical methods; however, it is worth noting that these methodologies may also possess inherent limitations.
RL-Based Adaptive Duty Cycle Scheduling in WSN-Based IoT Nets

The Internet of Things (IoT) is witnessing rapid adoption across various fields due to the advances in wire-less communication and low-power devices. However, achieving energy sustainability in IoT applications is a non-trivial task. Energy Neutral Operation (ENO) has emerged as a promising approach to address this issue. To this end, duty cycle scheduling is a prominent power management approach to attain ENO. The dynamic nature of the IoT environment poses a great challenge to determine individual nodes' duty cycle due to intermittent energy supply and variation in QoS requirements and traffic intensity. Artificial Intelligence (AI) and Machine Learning (ML) techniques can enhance QoS performance when integrated with ENO solutions. Therefore, our work considers employing Reinforcement Learning (RL) to compute the duty cycle of individual nodes based on the network's energy and traffic conditions. This work evaluates the performance of the RL solution in the context of multi-hop communication. The results are compared to a modified version of a regression-based duty-cycling solution from the literature.
Public Perception and Sentiment of ChatGPT: Machine Learning Analysis on Weibo Posts

ChatGPT, a large, pre-trained language model, which is developed by OpenAI, has exerted a great influence on society in all aspects since its launch. Knowing the adopters’ perspectives, preferences and sentiment is of critical significance for a technological company to promote and improve its products. Such social media as YouTube and Weibo, offer a convenient and time-saving channel for opinion mining. In this paper, 67,985 Weibo posts ranging from December 1, 2022 to September 30, 2023 were analyzed to explore the main topics and sentiment of users by employing machine learning techniques, including latent Dirichlet allocation (LDA) topic modeling algorithm and sentiment analysis. The results suggest that more than half of the adopters expressed a positive attitude towards all topics. However, a small portion of users have expressed worry and concern about the impact of ChatGPT on education and career compared to the other two topics, the development of the AI industry and the introduction of ChatGPT. This paper, while further proving the applicability of social media to explore the public’s opinion toward a new technology, offers insights into and contributes to the development and popularity of ChatGPT by figuring out Chinese users’ perspectives and sentiment.
Research on License Plate Recognition and Ranging Estimation Algorithm Based on Improved-YOLOv5 Target Detection Technique

Vehicle detection technology is important for realizing automatic monitoring and artificial intelligence-assisted driving systems. With the continuous development of artificial intelligence and automatic driving technology, there is an increasing demand for research on license plate recognition and distance prediction between vehicles. Existing research on target detection generally faces the problems of insufficient recognition accuracy, complex networks, and large resource utilization. To cope with this situation, this study creatively proposes an aggregation network for license plate recognition and distance estimation based on the Yolov5 network, which is capable of simultaneously recognizing license plates as well as predicting the distances between license plates and shooting points. The mobilenetV3 lightweight network is used as the backbone network of Yolov5, and the SE module of channel-separated convolution and attention mechanism is introduced for license plate information extraction, with a support vector machine model concatenated with the anchor point information of the image output from Yolov5 for the estimation of the distance to the front vehicle. The number of layers of the improved mobilenetV3 lightweight network is reduced by 60.7%. The RMSE when using it for distance prediction is within 3.6, which significantly improves the accuracy and speed of distance prediction when compared with the traditional monocular distance measurement method.
A Critical Analysis of Deep Learning Applications in Crop Pest Classification: Promising Pathways and Limitations

Deep Learning (DL) has recently emerged as a pivotal tool in a wide variety of agricultural end-level applications. While promising outcomes have been achieved in crop disease identification, the performance often falls short while accurately classifying crop pests. This study investigates the challenges of crop pest classification that are affecting the design and efficacy of Deep Neural Networks. The investigation involves a meticulous analysis of fundamental factors that influence the effectiveness of DL-based models. The literature utilizes a carefully chosen dataset, IP102, to reflect real-world challenges like class imbalance, complex backgrounds, multiple pest infestations, interclass similarity of pests, high intra-class variance, etc. This study unfolds the impact of the critical factors, ranging from dataset characteristics to the challenge of covariate shift, along with providing pathways to tackle them. Combining literature insights and practical experimentation, this study provides valuable perspectives on enhancing DL-based models for effective crop pest classification in real-world agricultural settings.
Efficiency Amelioration of Novel PM6:D18:L8-BO Bulk Heterojunction Organic Solar Cell by 1D Binary Photonic Crystal Integration

Organic solar cells (OSCs) are a promising candidate for next-gen photovoltaic devices due to their numerous attractive features. Despite recent progress, the efficiency is still stuck at ~19.2%, restraining this technology from being widely available. This manuscript discusses the performance amelioration strategy obtained by incorporating functionally designed (Nb<inf>2</inf>O<inf>5</inf>/MgF<inf>2</inf>)<sup>N</sup> one-dimensional photonic crystal (1D-PC) in bulk heterojunction (BHJ) OSC. Increasing the 1D-PC causes a narrower photonic bandgap and smaller refractive index difference, allowing light to reflect back into the SC at a wavelength that is similar to that absorbed by the PM6:D18:L8-BO. Using the Transfer Matrix Method (TMM) formalized optical simulation and a drift-diffusion-based electrical solver, the proposed BHJ organic thin-film photovoltaic cell with an In<inf>2</inf>O<inf>3</inf>:H/PEDOT:PSS/PM6:D18:L8-BO/AZO/((Nb<inf>2</inf>O<inf>5</inf>/MgF<inf>2</inf>)<sup>N</sup> (1D-PC)) structure has been designed and analyzed. The electrical performance parameters (V<inf>OC</inf>, J<inf>SC</inf>, FF, and PCE) and optical properties (reflectance, transmittance) are compared between cells with and without 1D-PC obtained under the highest performance values. The integration of the champion (Nb<inf>2</inf>O<inf>5</inf>/MgF<inf>2</inf>)<sup>3</sup> 1D-PC system to the BHJ-cell does not significantly alter V<inf>OC</inf>, but it does raise J<inf>SC</inf> from 20.92 mA/cm<sup>2</sup> to 23.97 mA/cm<sup>2</sup>, grows by 14.58%, and FF increases by 6.36% (87.63% to 93.20%). The observed improvement in J<inf>SC</inf> also leads to an increase in overall PCE, from 17.29% to 21.22%, an increase of 22.73%. Indeed, the efficiency amelioration technique disclosed herein is relevant to an immense audience and suggests the exciting potential and future advancement of cost-effective, efficient BHJ-OSC.
Analysis of Phasor Measurement Strategies for application in synthetic inertia response

The increasing integration of renewable energy sources into electrical grids, replacing conventional synchronous generators with inverter-based alternatives, poses challenges to grid stability due to diminishing effective rotational inertia. This paper focuses on the critical role of synthetic inertia in addressing stability issues during fault events. Specifically, it investigates the transient behavior of two phasor measurement units based on frequency lock loop and phase locked loop architectures The analysis compares the transient performance of the SOGI-FLL and DDSRF-PLL in terms of start-up behavior, total vector error and frequency estimation capability during disturbance events.
A Generative Adversarial Networks based Modelling for Efficient Design of Wind Energy Conversion Systems

Climate change and global energy crisis are two primary drivers behind the search of several forms of renewable energy sources e.g., wind energy. In studies of wind farm layout design and control, wind frequency map (WFM) plays a crucial role. WFM is a joint probability mapping between wind direction and speed considering their time series behavior. The limited past data suppresses the ability to represent long term variabilities in wind which leads to inaccurate estimation of WFMs resulting in unrealistic calculation of wind power for wind farms. Hence, in this paper, Generative Adversarial Networks (GANs) are explored for generation of wind frequency maps using limited wind time series data. GAN s are data driven probabilistic techniques, which captures distribution hidden in data. The distribution of wind scenarios, once captured by GANs, can generate new scenarios. The success of GAN's ability in capturing the probability distribution hidden in a wind dataset was demonstrated by calculating power production from an optimal layout using the available scenarios and scenarios generated by GANs. Trained GANs can generate many scenarios in future horizon which can be used for robust design of wind farms. Thus, this study can be helpful in efficient design and control of wind farms under wind state uncertainty.