Artificial intelligence (AI) is built into many products and has the potential to dramatically impact societies around the world. This short theoretical paper aims to provide a simple framework that might help us understand how the introduction and/or use of products with AI might influence the well-being of humans. It is proposed that considering the dynamic Interplay between variables stemming from Modality, Person, Area, Culture and Transparency categories will help to understand the influence of AI on well-being. The Modality category encompasses areas such as the degree of AI being interactive, informational versus actualizing, or autonomous. The Person variable contains variables such as age, gender, personality, technological self-efficacy, and perceived competence when interacting with AI, whereas the Area variable can comprise a certain product where AI is in-built or a certain domain where AI is used to make a difference (such as the health sector, military sector, education sector, etc.). The Culture variable is of importance to understand because cultural settings might shape attitudes towards AI. Finally, this might also be true for transparent AI (or understandable/explainable AI), with high degrees of transparency likely to elicit trust. The proposed model suggests that there is no easy answer when one seeks to understand the impact of AI on the world and humans. Only by considering a myriad number of variables in a model, summed up in the acronym IMPACT (Interaction of Modality-Person-Area-Culture-Transparency), we might get closer to an understanding of how AI impacts individuals' well-being. 

Considering the IMPACT framework to understand the AI-well-being-complex from an interdisciplinary perspective

Considering the IMPACT Framework to Understand the Ai-well-being-complex From an Interdisciplinary Perspective

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

The advent of attention-based architectures in medical imaging has ushered in an era of precision diagnostics, particularly in the detection and classification of brain tumors. This study introduced an innovative knowledge distillation framework employing a tripartite attention mechanism within transformer encoder models, specifically tailored for the identification of multiple brain tumor classes through magnetic resonance imaging (MRI). The proposed methodology synergistically harnesses the capabilities of large, highly parameterized teacher models to train more compact, efficient student models suitable for deployment in resource-constrained environments such as the internet of medical things and smart healthcare devices. Utilizing a diverse array of MRI sequences—including T1, contrast-enhanced T1, and T2—this study accounts for the nuanced variations across brain tumor classes derived from three extensive datasets. The tripartite attention mechanism addresses the limitation of traditional attention models by innovatively integrating temperature-softening neighborhood attention, global attention, and cross-attention layers. This sophisticated approach allows for a richer and more nuanced feature representation, capturing both local and global contextual information and intricate tumor features within MRI scans. This is supplemented by a unique augmentation pipeline and shifted patch tokenization technique, which enrich the model's input representation, especially for underrepresented classes. Through meticulous experimentation and ablation studies, the study demonstrates that the proposed model not only retains the robustness of its larger counterparts but also delivers enhanced performance metrics. When juxtaposed with benchmarking models—including traditional deep CNNs and various transformer-based architectures—the proposed model consistently showcases superior results. Its effectiveness is reflected in its lower teacher and student losses, commendable Brier scores, and noteworthy top-1 and top-5 accuracies, as well as AUC metrics across all datasets. This paper not only validates the efficacy of knowledge distillation in complex medical image analysis tasks but also provides a promising pathway for the integration of cutting-edge AI techniques in real-world clinical applications, potentially revolutionizing the early detection and treatment of brain tumors. 

Knowledge distillation in transformers with tripartite attention: Multiclass brain tumor detection in highly augmented MRIs

LAY ABSTRACT
The knowledge and attitudes of educators can have a strong influence on the experiences of autistic children who attend inclusive early childhood settings. Autistic children from under-represented ethnic groups, for example, tamariki takiwātanga Māori (autistic Māori children), are likely to face extra challenges and educators need to consider ways to support their cultural development. For this study, we interviewed 12 educators with recent experience supporting tamariki takiwātanga Māori in inclusive early childhood settings. We constructed three themes and seven subthemes from the interview data. We found that educators' understandings of autism were mostly in line with the neurodiversity perspective, which views autism as a difference, not a disorder. We also found similarities between the neurodiversity perspective and Māori perspectives of autism and a need for more training and resources based upon a Māori world view and available in te reo Māori (the Māori language).

https://journals.sagepub.com/doi/pdf/10.1177/13623613231181622

Supporting tamariki takiwātanga Māori (autistic Māori children): Exploring the experience of early childhood educators.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

Abstract This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database. 

Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

Teachers’ Educational Experiences and Preparedness in Teaching Students with Autism

Engineering Mathematics requires that problem-solving should be implemented through ongoing assessments; hence the prediction of student performance using continuous assessments remains an important task for engineering educators, mainly to monitor and improve their teaching practice. This paper develops probabilistic models to predict weighted scores (WS, or the overall mark leading to a final grade) for face-to-face (on-campus) and web-based (online) Advanced Engineering Mathematics students at an Australian regional university over a 6-year period (2013–2018). We fitted parametric and non-parametric D-vine copula models utilizing multiple quizzes, assignments and examination score results to construct and validate the predicted WS in independently test datasets. The results are interpreted in terms of the probability of whether a student’s continuous performance (i.e., individually or jointly with other counterpart assessments) is likely to lead to a passing grade conditional upon joint performance in students’ quizzes and assignment scores. The results indicate that the newly developed D-vine model, benchmarked against a linear regression model, can generate accurate grade predictions, and particularly handle the problem of low or high scores (tail dependence) compared with a conventional model for both face-to-face, and web-based students. Accordingly, the findings advocate the practical utility of joint copula models that capture the dependence structure in engineering mathematics students’ marks achieved. This therefore, provide insights through learning analytic methods to support an engineering educator’s teaching decisions. The implications are on better supporting engineering mathematics students’ success and retention, developing evidence-based strategies consistent with engineering graduate requirements through improved teaching and learning, and identifying/addressing the risk of failure through early intervention. The proposed methods can guide an engineering educator’s practice by investigating joint influences of engineering problem-solving assessments on their student’s grades.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09759284.pdf

Student Performance Predictions for Advanced Engineering Mathematics Course With New Multivariate Copula Models

Automated stress detection with explainable-AI transformer TabNet technique and physiological signals

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