B‐Tree is a well‐known indexing data structure for managing large persistent data. In its native form, it is used by database management systems and not humans. It is seen as a tree whose nodes are disk blocks. In this paper, an adjustment on B‐Tree is proposed to become easier to use for humans. This is done by relaxing the restriction that its node has to be a disk block. Instead, tree nodes are implemented as files. All B‐Tree algorithms were adjusted and implemented to adapt to this change. The main gain of this work is better usability of a B‐Tree for humans. Another gain is the complete freedom in setting the degree of the tree and the size of the data record. An experimental study was conducted to study how the performance of insertion and deletion changes with the degree of the tree t . It was found that the performance of the algorithms is very much affected by the number of levels of the tree. Very large values of t caused a degradation in performance. However, this degradation was found to be low compared with the change in t . This work opens the door for more research to enhance the usability of the B‐Tree: like supporting the adjusted B‐Tree with a GUI that makes it even easier to build and manipulate B‐Trees by a human.

The rapid proliferation of generative artificial intelligence and automated systems has radically transformed human interaction, introducing emerging forms of functional affective expression that profoundly challenge traditional biological definitions. These advancements rely on what the existing literature terms ‘artificial empathy’, a computational capacity for affect detection and proxy generation known for nearly 2 decades. However, the social sciences urgently require a broader structural categorization to evaluate its systemic implementation. Therefore, this study conceptually extends and systematizes this existing literature by proposing the advanced sociological construct of ‘Teleological Empathy’. The research is framed within a qualitative, theoretical‐constructive methodology, based on a critical documentary review of interdisciplinary sources from psychology, sociology, artificial intelligence, alignment ethics and the philosophy of language. Through abductive reasoning and robust theoretical triangulation, the study identifies categorical gaps in the current academic treatment of nongenuine, functional empathy. The main result is the formal definition of Teleological Empathy as a form of goal‐oriented and purposefully designed empathic expression that successfully fulfils specific, legitimate communicative functions in human or technological contexts, completely without requiring a subjective, biological affective experience on the part of the agent. To operationalize this construct, five key defining characteristics are rigorously distinguished: strategic intentionality, discursive or algorithmic design, absence of internal emotional experience, contextual coherence and functional legitimacy. Moving beyond the outdated ontological debate of ‘real versus fake’» empathy, this proposal provides specific operationalization guidelines for future empirical validation, ultimately offering a robust analytical framework specifically designed to evaluate interactional ethics, value alignment and organizational standardization in an increasingly automated world.

Alzheimer’s disease (AD), a progressive neurodegenerative disorder whose symptoms become apparent late in the disease process but are only in the early stages of development, creates challenges that demand that this disorder be diagnosed early to reduce its progression. This research work has also suggested a lifelong learning system that insists on the combination of MRI and spike neural signals (EEG data) for the early detection of AD using automated deep learning. By recurrent catastrophic forgetting through elastic weight consolidation (EWC) and memory replay, the model learns from new data while retaining past knowledge that is vital in health‐care‐related environments where patient information is ever‐expanding. Three levels of integration between MRI and spike neural data have been used in this work: early, mid‐, and late fusion. Experimental results show that mid‐fusion gives better performance than other approaches of 86% accuracy, 84% sensitivity, and 88% specificity for AD identification, which can provide the structure of MRI and temporal EEG signals to identify the AD patient. Early fusion proved a capability to integrate general MRI‐EEG correspondences effectively, as integrated late fusion showed the capacity for handling diverse qualities of inputs by analyzing both models independently. The results of the study affirm the effectiveness of the continuous learning multimodal fusion strategy in improving both sensitivity to early AD biomarkers and data heterogeneity. The proposed approach seems to be promising for scalable and real‐time diagnostic solutions suitable to bring heterogeneous clinical data and the incremental nature of medical data into the diagnosis of early‐stage AD and patient management.

As the digital revolution continues to take hold in contemporary society, new technology and communications networks have provided football with new possibilities and prospects for expansion. This study provides an assessment of the published research regarding innovative digital tools designed to increase the interactivity of fans when watching a football match, regardless of whether they do it at home or at the stadium. A systematic review of the literature was performed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The search was conducted in the PubMed, Web of Science, and Scopus databases. The final sample included eleven studies for analysis. Overall, the investigations that structure this review seem to be in the early stages of their development, with eight of them making tests with the target audience and the other three still in protocol development processes. Six studies concluded that fans had positive and exciting experiences using mobile applications or interactive systems. Two studies showed promising results in the area of football fans’ health, and only one study showed some difficulties for fans using an ad hoc network in the stadium. Adding personal information, fan interaction systems, specific information about the players and the teams’ tactical strategies, and interactive fan voting seem to be important elements for designing a successful interactive tool that contributes to increasing fans’ enthusiasm during football matches.

This research aims to (1) synthesize the digital painting learning process by using mixed-reality technology to develop practical skills in character design for animation and (2) develop and evaluate digital painting learning models using mixed-reality technology to develop practical skills in character design for animation. This research was conducted by studying documentation and related research and designing digital painting learning models using mixed-reality technology to develop practical skills in character design for animation. Using a questionnaire, a total of 9 experts evaluated the model’s suitability. The statistics used in the research were the mean and standard deviation. The study found that a component of the digital painting learning process involves using mixed-reality technology to develop practical skills in character design for animation. It consists of the following four components: (1) input data, (2) the digital painting learning process, (3) output assessment, and (4) feedback. The expert’s evaluation of the model’s suitability found that the digital painting learning model using mixed-reality technology to develop practical skills in character design for animation was at the highest level (mean ± SD = 4.38 ± 0.21). This research is beneficial to faculty who teach character design and development for animation. The results of the study can be applied to teaching and learning.

The number of children with mental health problems is ever-growing; as a result, nearly 850,000 children in the UK are believed to have clinically significant problems, and only a quarter show evidence of mental illness. Family members often have a hard time dealing with children with mental health problems. As a result, digital mental health interventions are becoming popular for people seeking professional mental health services. Previous studies in this area have also shown that parents who are divorced or working away from home struggle to maintain contact with their children. This lack of communication between the parents and their children can worsen the children’s mental health conditions and prevent early diagnosis. Human-centred design thinking is applied step by step in this paper to provide an intuitive understanding of the design process. Five stages of the design thinking process were examined to follow a correct path. The results were promising, and the feedback received assured that the product helps parents to better monitor their children’s mental health and provides support when needed. The design thinking process was followed in concordance with the user needs identified from previous studies in this area, which led to a working solution that benefits both parents and children in tackling these problems.

The paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. Related research work in the field of imagined speech has revealed that imagined speech decoding performance and accuracy require attention to further improve. The evolution of deep learning technology increases the likelihood of decoding imagined speech from EEG signals with enhanced performance. We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. The experiment was carried out using an open-access dataset of fifteen subjects’ imagined speech multichannel signals of vowels and words. The raw multichannel EEG signals of multiple subjects were processed using discrete wavelet transformation technique. The model was trained and evaluated using the preprocessed signals, and the model hyperparameters were adjusted to achieve higher accuracy in the classification of imagined speech. The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. The results of the study indicate that individuals with speech difficulties might well be able to leverage a noninvasive EEG-based imagined speech brain-computer interface system as one of the long-term alternative artificial verbal communication mediums.

Midair gestural interaction has gained a lot of attention over the past decades, with numerous attempts to apply midair gestural interfaces with large displays (and TVs), interactive walls, and smart meeting rooms. These attempts, reviewed in numerous studies, utilized differing gestural techniques for the same action making them inherently incomparable, which further makes it difficult to summarize recommendations for the development of midair gestural interaction applications. Therefore, the aim was to take a closer look at one common action, translation, that is defined as dragging (or moving) an entity to a predefined target position while retaining the entity’s size and rotation. We compared performance and subjective experiences (participants = 30) of four midair gestural techniques (i.e., by fist, palm, pinch, and sideways) in the repetitive translation of 2D objects to short and long distances with a large display. The results showed statistically significant differences in movement time and error rate favoring translation by palm over pinch and sideways at both distances. Further, fist and sideways gestural techniques showed good performances, especially at short and long distances correspondingly. We summarize the implications of the results for the design of midair gestural interfaces, which would be useful for interaction designers and gesture recognition researchers.

Gender classification from human face images has attracted researchers over the past decade. It has great impact in different fields including defense, human-computer interaction, surveillance industry, and mobile applications. Many methods and techniques have been proposed depending on clear digital images and complex feature extraction preprocessing. However, most recent critical real systems use thermal cameras. This paper has the novelty of utilizing thermal images in gender classification. It proposes a unique approach called IRT_ResNet that adopts residual network (ResNet) model with different layer configurations: 18, 50, and 101. Two different datasets of thermal images have been leveraged to train and test these models. The proposed approach has been compared with convolutional neural network (CNN), principal component analysis (PCA), local binary pattern (LBP), and scale invariant feature transform (SIFT). The experimental results show that the proposed model has higher overall classification accuracy, precision, and F-score compared to the other techniques.

The honeyword system is a password cracking detection technique that aims to improve the security of hashed passwords by making password cracking simpler to detect. Many honeywords (false passwords) accompany the sugarword (true password) to form the sweetwords (false and true passwords) for every user. If the attacker signs in using a honeyword, a silent alarm trigger shows that the honeyword system might be compromised. Many honeyword generation techniques are presented; each one has a flaw in the generating process, a lack of support for all honeyword characteristics, and a slew of honeyword problems. The harmony search algorithm (HSA), a metaheuristic intelligence algorithm inspired by music, is used in this article to offer a novel method for generating honeyword. The suggested honeyword generation technique will enhance the generating process, enhance honeyword characteristics, and address prior approaches’ shortcomings. This paper will show several previous honeyword generation techniques, explain the suggested one, discuss the experimental findings, and compare the new honeyword generation method with the previous ones.