Abstract: Education is an activity that involves great cognitive load for learning, understanding, concentrating and other high level cognitive tasks. The use of the electroencephalogram (EEG) and other brain imaging techniques in education has opened the scientific field of neuroeducation. Insights about the brain mechanisms involved in learning and assistance in evaluation and optimization of education methodologies according to student brain responses is the main target of this field. Being a multidisciplinary field, neuroeducation requires expertise in various fields such as education, neuroinformatics, psychology, cognitive science and neuroscience. The need for a comprehensive guide where various important issues are presented and examples of their application in neuroeducation research projects are given is apparent. This paper presents an overview of the current hardware and software options, discusses methodological issues, and gives examples of best practices as found in the recent literature. These were selected applying the PRISMA statement on results returned by searching in PubMed, Scopus and Google Scholar with keywords “EEG and neuroeducation” for projects published on the last six years (2018-2024). Apart from the basic background knowledge, two research questions regarding methodological aspects (experimental settings, hardware and software used) and the subject of research and type of information used from the EEG signals are addressed and discussed.

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In today’s digital age, ethical issues are a principal concern during responsible data research which require thoughtful consideration and approaches for managing them. As technological advances materialize to increase the standard of living for people across the world, ethics must be prioritized by cybersecurity professionals to ensure these technologies are not being misused or inflicting harm to the general population especially to underrepresented and underprivileged communities. Cybersecurity subject matter experts must develop awareness regarding ethical ramifications of their research endeavors to ensure security is balanced with moral standards. Observance of and adherence to ethical based policies, principles, and security best practices will delineate cybersecurity professionals from threat actors.

Abstract: This paper definitively settles the longstanding conjecture regarding odd perfect numbers. A perfect number is one whose sum of divisors equals itself doubled. While Euclid's method for constructing even perfect numbers is well-known, the existence of odd ones has remained elusive. By employing elementary techniques and analyzing the properties of the divisor sum function, we conclusively prove that no odd perfect numbers exist.

Abstract: Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, such as range-fast Fourier transform (FFT) based time-range maps, time-doppler based short-time Fourier transform (STFT) maps, and smoothed pseudo Wigner-Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity, and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2 combined with STFT preprocessing showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing.

Abstract: Depth estimation is a key technology in autonomous driving, as it provides an important basis for accurately detecting traffic objects and avoiding collisions in advance. To enhance depth estimation performance in complex traffic environments, this study proposes a depth estimation method in which point clouds and images obtained from MMwave radar and cameras are fused. Firstly, a residual network is established to extract the multi-scale features of the MMwave radar point clouds and the corresponding image obtained simultaneously from the same location. Correlations between the radar points and the image are established by fusing the extracted multi-scale features. A semi-dense depth estimation is achieved by assigning the depth value of the radar point to the most relevant image region. Secondly, a bidirectional feature fusion structure with additional fusion branches is designed to enhance the richness of feature information. The information loss during the feature fusion process is reduced, and the robustness of the model is enhanced. Finally, parallel channel and position attention mechanisms are used to enhance the feature representation of key areas in the fused feature map; the interference of irrelevant areas are suppressed, and the depth estimation accuracy is enhanced. Experimental results on the public dataset nuScenes show that, compared with the baseline model, the proposed method reduces the average absolute error (MAE) by 4.7%-6.3% and the root mean square error (RMSE) by 4.2%-5.2%.

Abstract: The cart-pole application is a well-known control application that is often used to illustrate reinforcement learning algorithms with conventional neural networks. An implementation of the application from OpenAI Gym is ubiquitous and popular. In this paper, we explore using this application as a benchmark for spiking neural networks. We propose four parameter settings that scale the application in difficulty, in particular beyond the default parameter settings which do not pose a difficult test for AI agents. We propose achievement levels for AI agents that are trained on these settings. Next, we perform an experiment that employs the benchmark and its difficulty levels to evaluate the effectiveness of eight neuroprocessor settings on success with the application. Finally, we perform a detailed examination of eight example networks from this experiment, that achieve our goals on the difficulty levels, and comment on features that enable them to be successful. Our goal is to help researchers in neuromorphic computing to utilize the cart-pole application as an effective benchmark.

Abstract: Ramsey theory is applied to the analysis of operators acting on the functions belonging to the L^2 Hilbert space. The operators form the vertices of the bi-colored graph. If the operators commute, they are connected by a red link; if the operators do not commute they are connected with a green link. Thus, the complete, bi-colored graph emerges and the Ramsey theory becomes applicable. If the graph contains six vertices/operators, at least one monochromatic triangle will necessarily appear in the graph. Thus, the triad of operators forming the read triangle possesses the common set of eigenfunctions. The extension of introduced approach to infinite sets of operators is addressed. Applications of the introduced approach to problems of classical and quantum mechanics are suggested.

Abstract: Representing and interpreting human opinions within an unstructured framework is inherently complex. Hesitant fuzzy linguistic term sets offer a comprehensive context that facilitates a nuanced understanding of diverse perspectives. This study introduces a methodology that integrates sentiment analysis with hesitant fuzzy linguistic term sets to effectively aggregate and compare news from diverse sources. By employing linguistic scales, our approach enhances the interpretation of various perceptions and attitudes, thus facilitating comprehensive knowledge extraction and representation. The main objective of this research is to conduct a comparative analysis of news coverage across European countries in relation to the Israel-Gaza war. This analysis aims to explore the different perceptions and attitudes surrounding the ongoing situation, highlighting how different nations perceive the conflict.

Abstract: Artificial Intelligence (AI) has recently had unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of design and development of Computer Aided Diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adaptation and integration in the healthcare system still seems far-fetched. Diagnostic Radiology is no exception. Imagining techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) scans have been vastly and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI powered systems, to analyze such scans, have been incorporated into the standard operating procedures in healthcare system. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of Explainable Artificial Intelligence (XAI), the old school black boxes of Deep Learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. We also summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity.

Abstract: In this paper, we examine a nonlinear partial differential equation in complex time t and complex space z combined with so-called Mahler transforms acting on time. This equation is endowed with a leading term represented by some infinite order formal differential operator of irregular type which enables the construction of a formal power series solution in t obtained by means of a Borel-Laplace procedure known as k-summability. The so-called k-sums are shown to solve some related differential functional equations involving integral transforms which stem from the analytic deceleration operators appearing in the multisummability theory for formal power series.