Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers

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Deep neural networks form the backbone of artificial intelligence (AI) research, with potential to transform the human experience in areas ranging from autonomous driving to personal assistants, healthcare to education. However, their integration into the ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the urgent need for innovative paradigms in urban development. In response, sustainable smart cities are increasingly leveraging advanced technological fram ...

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there ...

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Through a physics-based model framework, the authors propose a central role for the nonequilibrium processes underling gene activity in shaping morphology, dynamics, and regulation of diverse nuclear condensates. Condensation by phase separation has recent ...

Objective. Artificial vision has been and still is the subject of intense research. The ultimate goal is to help blind people in their daily life. Approaches to artificial vision, including visual prostheses and optogenetics, have strongly focused on resto ...

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude compre ...

Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor–patient relationships, trust is therefore regularly suggested as a way of dealing ...

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve ...

As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of sustained exposure. Since the affective content of speech is inherently modul ...

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities ...

Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning ...

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

Power gating is a common approach for reducing circuit static power consumption. In FPGAs, resources that dominate static power consumption lie in the routing network. Researchers have proposed several heuristics for clustering multiplexers in the routing ...

Network alignment is the task of recognizing similar network nodes across different networks, which has many applications in various domains. As traditional network alignment methods based on matrix factorization do not scale to large graphs, a variety of ...

We study the problem of constructing epsilon-coresets for the (k, z)-clustering problem in a doubling metric M(X, d). An epsilon-coreset is a weighted subset S subset of X with weight function w : S -> R->= 0, such that for any k-subset C is an element of ...

Cells rely on focal adhesions (FAs) to carry out a variety of important tasks, including motion, environmental sensing, and adhesion to the extracellular matrix. Although attaining a fundamental characterization of FAs is a compelling goal, their extensive ...

A growing body of research on statistical applications for characterization of atmospheric aerosol Fourier transform infrared (FT-IR) samples collected on polytetrafluoroethylene (PTFE) filters (e.g., Russell et al., 2011; Ruthenburg et al., 2014) and a ri ...

Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers

Chattez avec Graph Search

A Human-Centric Approach to Explainable AI for Personalized Education

Improving the accuracy and interpretability of neural networks for wind power forecasting

Artificial intelligence of things for sustainable smart city brain and digital twin systems: Pioneering Environmental synergies between real-time management and predictive planning

Personalized Clustering for Emotion Recognition Improvement

Identification of typical district configurations: A two-step global sensitivity analysis framework

A model for organization and regulation of nuclear condensates by gene activity

The role of the visual field size in artificial vision

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Misplaced Trust and Distrust: How Not to Engage with Medical Artificial Intelligence

Intentional machines: A defence of trust in medical artificial intelligence

Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE

Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load

Quantifying Explainers of Graph Neural Networks in Computational Pathology

Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

A machine learning approach for power gating the FPGA routing network

Network Alignment by Representation Learning on Structure and Attribute

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics

Investigating Focal Adhesion Substructures by Localization Microscopy

An automated baseline correction protocol for infrared spectra of atmospheric aerosols collected on polytetrafluoroethylene (Teflon) filters