We created a unique dataset for Brain Computer Interface (BCI) system development by recording brain activity from a dedicated volunteer pronouncing 100 specially selected Malayalam words and English translations relevant to astronauts during human space missions. These words were spoken vocally and subvocally 50 times each, utilizing non-invasive Electroencephalography (EEG) sensors. This dataset paves the way for future BCI applications, potentially transforming communication for astronauts and individuals with limitations.

This systematic literature review (SLR) explores the broad application of Artificial Intelligence (AI) in healthcare, with a focus on integrating emotional and cognitive analysis techniques. The review aims to assess the impact of AI technologies on enhancing patient care by analyzing emotional content and supporting patient-centric applications. We examine studies that leverage AI for sentiment analysis, emotion categorization, and predictive modeling using textual data from clinical narratives, patient feedback, and online health discussions. The review identifies key advancements in emotion recognition algorithms, the synergy of cognitive and emotional analysis methods, and the development of AI-powered clinical decision support systems. These applications have significantly improved personalized therapy plans, facilitated early detection of mental health disorders, and enhanced support for clinical interventions. Despite these developments, challenges such as ethical AI implementation, patient confidentiality, and algorithmic biases remain. Nevertheless, AI's potential to transform healthcare practices is evident, promising a future of more informed, efficient, empathetic, and patient-centered healthcare. This study highlights AI's transformative impact on healthcare by providing a comprehensive overview of its role in emotional and cognitive analysis, identifying emerging trends, and advocating for the integration of AI capabilities with the human aspects of healthcare.

Neural networks (NNs) have been extensively adapted to various security tasks, such as spam detection, phishing, and intrusion detection. Particularly in IDS, NNs face significant vulnerabilities to adversarial attacks, where the adversary attempts to exploit the fragilities within machine learning (ML) models. This study introduces a novel approach using interval-bound propagation (IBP) to formally verify and enhance the resilience of both shallow and deep NNs. We also investigated the effectiveness of various activation functions using benchmark IDS datasets. Our findings show that ReLu and Leaky-ReLu functions enhance resistance in shallow networks, whereas Tanh functions perform better in deep networks. We provide certified accuracies for models subjected to various input perturbations ranging from 0.0001 to 0.9, marking a significant advancement in verifying the security of NNs.

We propose a new type-based protograph raptor-like LDPC code for rate-adaptive information reconciliation in CV-QKD systems. It offers robust error-correction performance across a wide range of rates while simplifying the code design process.

Gas sensors based on two-dimensional (2D) materials have gained significant research attention due to their numerous advantageous properties, including a high surface-to-volume ratio, exceptional electrical conductivity, tunable electronic properties, mechanical flexibility, and potential for functionalization to enhance sensitivity and selectivity. In this paper, we employ first-principles calculations to conduct a predictive study on the gas-sensing properties of a recently proposed novel 2D phase of Gallium Oxide monolayer, with a unit cell formula of Ga 2 O 2 [L. Shao et al., Physical Chemistry Chemical Physics, 23(1), 666-673, 2021]. Our findings illustrate that the monolayer exhibits remarkable sensitivity and selectivity for detecting NH 3 molecules. Additionally, although NO demonstrates comparatively lower adsorption energy on the monolayer, it reveals exceptionally favorable attributes for sensitivity, making the monolayer suitable for detecting NO molecules at lower temperatures. The low adsorption energy of the monolayer towards ambient molecules makes it suitable for deployment in ambient environments. The monolayer also shows potential for enhanced NH 3 sensitivity through application of appropriate strain. Thus, the Ga 2 O 2 monolayer emerges as a promising candidate for the fabrication of commercial 2D gas sensors.

One of the elements influencing users’ perception of video quality is the viewing distance. Although TVs have grown in size and resolution, viewers’ watching behavior and living room conditions have not changed equally and therefore viewers sit at distances farther than optimal. This gives rise to the question whether higher resolutions are perceptually beneficial in such scenarios. Therefore, this paper presents four studies that investigate the improvements offered by 8K / UHD2 in comparison to 4K / UHD-1 using HDR videos with viewing distances between 0.50 H and 3.00 H with H representing the screen height. Study 1 identifies general effects while Study 2 refines the methodology and aims at finding the distance of subjective quality parity. Study 3 includes encoded contents to better represent a real-world streaming scenario. Study 4 looks into the effects of watching contents once or repeatedly per test and includes a wider variety of self created sources. The findings of the studies provide quantitative data on the effect of viewing distance, showing how the slight video quality advantage of 8K HDR against 4K HDR content at the design viewing distance of 0.80 H diminishes as the viewing distance increases, with no or very little improvements beyond 2.60 H, depending on the content. In general, the degree of enhancement is related to the spatial and temporal content complexity, as well as the bitrate settings employed for encoding. Additionally, it is found that, on average, subjects prefer to sit at viewing distances of around 2.20 H. For most people this may be too far away to truly appreciate the improvements offered by the 8K resolution.

This paper will assess the different principles of maintainable software and give insight on why they are necessary while writing software. Throughout this paper we will access these practices by writing code examples, by following these practices and sometimes deliberately not following them or sometimes just discussing in general why these practices should be followed, creating a sense for measuring maintainability in software. At the end we will develop a generalized file system for Software that encourages maintainability while applying key principles discussed.

The trend of global economic integration has fostered the prosperity of the maritime transportation industry, which has placed higher demands on the construction of automated container terminals, and the optimization of the integrated  berth allocation and crane assignment problems (BACAPs) is a key link of. Currently, population-based computational intelligence methods have attracted attention on BACAPs, but on small-scale case and simplified problem models. In this paper,  we propose a novel swarm optimization with intra- and inter-hierarchical competition (I2HCSO) for addressing large-scale BACAPs, which is a major challenge in container terminals. First, we construct a hierarchical model with better particles at higher hierarchy, and the population at different hierarchy are divided into several sub-swarm. Then, we design a intra- and inter-hierarchical competitive mechanism to balance the exploration and exploitation of the population, in which intra-hierarchical competition is carried out within sub-swarm at any hierarchy, whereas inter-hierarchical competition occurs in different sub-swarms of neighboring hierarchies. Third, we consider optimizing the priorities of vessels for efficient use of resources berth and crane for the first time in BACAPs, and employ ε-constraints to search for feasible regions. Additionally, we develop a local search operator as a repair strategy to improve the quality of the solution. Finally, we test I2HCSO in a set of cases consisting of 25 BACAPs. Compared with the several typical optimizers with experimental results, I2HCSO is more competitive on BACAPs with different scales.

Automatic and standardized methods for 4D transesophageal echocardiography (TEE) annotation and mitral valve (MV) motion and morphology analysis are difficult to achieve due to the known limitations of echocardiography and the limited availability of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo-labelling to achieve MV segmentation in 4D TEE. The proposed strategy is based on a Teacher-Student framework that guarantees the generation of pseudo-labels with high reliability. 120 4D TEE recordings from 60 candidates to MV repair are used. The Teacher model, an ensemble of three convolutional neural networks (CNNs), is trained on end-systole (ES) and end-diastole (ES) frames and is used to generate MV pseudo segmentations on intermediate frames of the cardiac cycle. The pseudo annotated frames augment the training set of the Student model, enhancing its segmentation accuracy and temporal consistency. The Student model achieves superior performance than individual Teacher models with Dice Score of 0.82, average surface distance of 0.37 mm and 95% Hausdorff distance of 1.72 mm for the MV leaflets, and lower coordinate predictions error for the mitral annulus. The Student model demonstrates reliable frame-by-frame MV segmentation, accurately identifying leaflet morphology and dynamics throughout the cardiac cycle, with a significant reduction in inference time compared to the Ensemble model. This approach significantly reduces the manual annotation workload and ensures reliable, repeatable, and time-efficient MV analysis. Our method holds strong potential for improving the accuracy and efficiency of MV diagnostics and treatment planning in clinical settings.

Modern real-time systems often involve numerous computational tasks characterized by intricate dependency relationships. Within these systems, data propagate through cause-effect chains from one task to another, making it imperative to minimize end-to-end latency to ensure system safety and reliability. In this paper, we introduce innovative non-preemptive scheduling techniques designed to reduce the worst-case end-to-end latency and/or time disparity for task sets modeled with directed acyclic graphs (DAGs). This is challenging because of the non-continuous and non-convex characteristics of the objective functions, hindering the direct application of standard optimization frameworks. Customized optimization frameworks aiming at achieving optimal solutions may suffer from scalability issues, while general heuristic algorithms often lack theoretical performance guarantees. To address this challenge, we incorporate the ''1-opt" concept from the optimization literature (Essentially, 1-opt means that the quality of a solution cannot be improved if only one single variable can be changed) into the design of our algorithm. We propose a novel optimization algorithm that effectively balances the trade-off between theoretical guarantees and algorithm scalability. By demonstrating its theoretical performance guarantees, we establish that the algorithm produces 1-opt solutions while maintaining polynomial run-time complexity. Through extensive large-scale experiments, we demonstrate that our algorithm can effectively reduce the latency metrics by 20% to 40%, compared to state-of-the-art methods.

This paper presents a comprehensive wideband indoor propagation characterization via real-world experiments at 105.8 GHz with a 4 GHz bandwidth in a lecture hall environment. Utilizing a double-directional channel sounding system with rotating horn antennas, we measure power angular delay profiles (PADPs) at multiple receiver (RX) positions. Our study derives a path loss model, delay characteristics, and angle characteristics, including path loss exponent, root mean square (RMS) delay spread (DS), K-factor, RMS azimuth angle spread of arrival (ASA), and RMS azimuth angle spread of departure (ASD). We compared these results with the 3GPP indoor hotspot (InH) office model (up to 100 GHz), highlighting both similarities and differences in propagation characteristics. Notably, the path loss exponent was found to be 1.38, which is lower than the 1.73 observed in the 3GPP InH-Office LoS model. Moreover, there were positive correlations between the above spread values and transmitter (TX)-RX distance, and negative correlations between K-factor and TX-RX distance, which were not considered in the 3GPP InH-Office LoS model. Furthermore, our analysis revealed a strong negative correlation in RMS ASA vs K-factor and RMS ASD vs K-factor, which are not accounted for in the current 3GPP InH office model. These findings emphasize the need to consider such correlations in accurate propagation models in lecture hall scenarios at the 105 GHz sub-THz band.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

In the 1990s, the HIV/AIDS epidemic raged without the effective control provided by today’s antiretroviral therapies (ARTs). Researchers desperately explored various approaches to combat the spread and devastation of the disease. This previously unreported pilot study of an autologous therapeutic vaccine (ATV) for HIV/AIDS in 9 children and 1 adult exemplifies the innovative spirit of that era. This study aimed to develop a patient-specific ATV to trigger a more robust immune response against HIV/AIDS and (at the time) prolong survival. Though 25 years old, the study remains the only ATV therapeutic vaccine clinical trial in children, offering valuable insights into the development of novel therapies against emerging infectious diseases.

This study investigated the performance of various metaheuristic algorithms in Procedural Content Generation, specifically focusing on generating game map layouts. It challenges the common use of Genetic Algorithms by exploring alternative optimization approaches for PCG tasks. Three metaheuristic algorithms: Genetic algorithms, Particle Swarm Optimization, and Artificial Bee Colony were compared for their effectiveness and efficiency in generating game levels. Comprehensive experiments were conducted by using each algorithm for map creation. Metrics such as the convergence speed and content quality were used to evaluate the generated maps and identify the strengths and weaknesses of each algorithm in this context. The findings reveal that GA using tournament selection outperform other GAs implementations such as ABC and PSO for generating game maps. This leads to the conclusion that the choice of the optimal metaheuristic algorithm for PCG depends on the specific task and the selected evaluation methods. In addition, this study suggests that integrating diverse metaheuristic approaches can enrich the field of PCG and inspire game developers to utilize a broader range of techniques. By incorporating various algorithms, developers can potentially enhance content generation and create more immersive and engaging game experiences.

The proliferation of tobacco-related content on social media platforms poses significant challenges for public health monitoring and intervention. This paper introduces a novel multi-modal deep learning framework named Flow-Attention Adaptive Semantic Hierarchical Fusion (FLAASH) designed to analyze tobacco-related video content comprehensively. FLAASH addresses the complexities of integrating visual and textual information in short-form videos by leveraging a hierarchical fusion mechanism inspired by flow network theory. Our approach incorporates three key innovations, including a flow-attention mechanism that captures nuanced interactions between visual and textual modalities, an adaptive weighting scheme that balances the contribution of different hierarchical levels, and a gating mechanism that selectively emphasizes relevant features. This multi-faceted approach enables FLAASH to effectively process and analyze diverse tobacco-related content, from product showcases to usage scenarios. We evaluate FLAASH on the Multimodal Tobacco Content Analysis Dataset (MTCAD), a large-scale collection of tobacco-related videos from popular social media platforms. Our results demonstrate significant improvements over existing methods, outperforming state-of-the-art approaches in classification accuracy, F1 score, and temporal consistency. The proposed method also shows strong generalization capabilities when tested on standard video question-answering datasets, surpassing current models. This work contributes to the intersection of public health and artificial intelligence, offering an effective tool for analyzing tobacco promotion in digital media.

In the field of information technology, cybersecurity is essential and one of the main concerns of today is information security. Ransomware is one of the more concerning cyber threats that is always evolving. This type of cyberattack entails encrypting the data or files belonging to the victim and requesting payment for the decryption process. Ransomware occurrences continue to increase in frequency, inflicting substantial disruption and financial loss despite the various cybersecurity precautions implemented by governments and organizations. This study does a thorough analysis of the literature on the creation and application of ransomware exploits. It looks at the techniques used to make ransomware, the weaknesses that are exploited, and how ransomware strategies have changed over time. This paper attempts to highlight areas for further research and uncover common trends in the evolution of ransomware exploits by synthesizing findings from multiple investigations. The assessment also covers defense tactics, new difficulties in battling this constantly changing danger, and the life cycle of ransomware attacks. This thorough investigation aims to advance our knowledge of ransomware exploit techniques and support the creation of stronger cybersecurity defenses.

Unmanned Aerial Vehicles (UAVs) are pivotal in various applications, yet their widespread use exposes them to significant cybersecurity threats, including cyber, physical, and hybrid attacks. This paper presents a comprehensive review of current research and advancements in UAV cybersecurity. Key vulnerabilities such as GPS spoofing, jamming, malware, and physical attacks are discussed, highlighting their potential impact on UAV operations and data security. The review identifies innovative defense strategies and frameworks proposed to mitigate these threats, including advanced penetration testing techniques, dynamic defense frameworks, and algorithms like Stackelberg games and swarm intelligence. Addressing specific research questions, the paper explores recent advancements in the use of advanced penetration testing techniques to enhance UAV security, evaluates the effectiveness of penetration testing methodologies across different UAV components, and analyzes challenges associated with conducting such tests and potential mitigation strategies. Furthermore, the paper discusses future directions in UAV cybersecurity, emphasizing the integration of emerging technologies such as quantum computing resilience, adaptive defense mechanisms, and secure software-defined networking (SDN). These advancements aim to bolster UAV defenses, ensuring robust protection against current and future cybersecurity challenges in dynamic and hostile operational environments.

Contribution: The purpose of this study is to identify students' early misconceptions in the use of variables in a firstyear computer programming course using Python. Background: Misconceptions start early, persist during the semester and prevent many students to make adequate progress to learn more advanced programming concepts. Research questions: Which are the most common misconceptions in the use of variables and the assignment operator in a first-year computer programming course using Python? What learning objectives and activities can guide instruction and scaffold students' progress to prevent or correct those early misconceptions? Methodology: We assessed student performance (n=108) in four different conceptual categories: problem understanding, variable assignment, data types, and arithmetic expressions. Assessments were implemented as multiple choice and short answer questions that involved reading and/or writing small flowcharts or snippets of Python code. Findings: We have identified prevalent students' misconceptions in problem understanding, variables and assignment operator syntax and semantics, data types, and arithmetic expressions in a first-year programming course using Python. To guide instruction and scaffold students' progress to prevent or correct those early misconceptions, we propose a set of activities organized around specific learning objectives aimed at correcting those misconceptions early on.

Many computing services need to be accessed through the Internet, which makes them inherently exposed to a large number of cybersecurity threats. Several attacks are thwarted by intrusion prevention mechanisms, but with the size and complexity of current systems, an attacker can break in sooner or later. Attackers can exploit a system vulnerability or steal user access credentials. Once the intrusion occurs, it is very likely that the state of the system is corrupted by the attacker. The classical solution against these corruptions is to keep regular backups to allow the reversal of the effects of intrusions. However, changes to the state made since the last backup are permanently lost. This situation has led to the development of work on intrusion recovery systems that revert the effect of attacks on the state of the system without losing legitimate changes. We present a survey of the literature in the area, explaining the different approaches and suggesting open directions for future research.