Dek : Adopting machine personhood risks a political and moral realignment built on illusion rather than evidence.

Many scientific fields of study use formally established reporting standards to foster research and experimental design, transparency, replicability, peer review, and student training. Examples include CONSORT in medicine, the What Works Clearinghouse in education, JARS in psychology. Such standards yield agreement on study reporting and evaluation, even if using different methodologies. CHI has not adopted reporting standards. Like other fields, CHI has seen an increased number of low-quality submissions and reviews fueled by AI. This panel’s objective is to discuss advantages and barriers of adopting reporting standards for SIGCHI. Panelists include representatives with significant experience creating, adopting and operationalizing reporting standards in adjacent fields: software engineering, CS education, and Programming Languages. The panel will include an overview of the history of reporting standards, a live demo of a standards-based peer review system, discussions of opportunities, challenges, limitations for SIGCHI reporting standards, and an interactive discussion between attendees and panelists.

The IEEE Computer Society has established and continuously evolved the Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) as an organized and authoritative collection of software engineering knowledge areas (KAs) and knowledge items since the 1990s. In this paper, we provide an overview of the SWEBOK Guide and briefly summarize its evolution history from its initial development to the latest version. Furthermore, we envision the future evolution of the SWEBOK Guide and its related community-building, as well as the certification program.

This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have little impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A counterfactual inspection analysis is strongly recommended to ensure a robust examination of counterfactual explanations and the potential identification of biases.

X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating the integration of structured clinical features with radiology reports. To address this, we introduce DALL-M, a novel framework that enhances clinical datasets by generating contextual synthetic data. DALL-M augments structured patient data, including vital signs (e.g., heart rate, oxygen saturation), radiology findings (e.g., lesion presence), and demographic factors. It integrates this tabular data with contextual knowledge extracted from radiology reports and domain-specific resources (e.g., Radiopaedia, Wikipedia), ensuring clinical consistency and reliability. DALL-M follows a three-phase process: (i) clinical context storage, (ii) expert query generation, and (iii) context-aware feature augmentation. Using large language models (LLMs), it generates both contextual synthetic values for existing clinical features and entirely new, clinically relevant features. Applied to 799 cases from the MIMIC-IV dataset, DALL-M expanded the original 9 clinical features to 91. Empirical validation with machine learning models - including Decision Trees, Random Forests, XGBoost, and TabNET - demonstrated a 16.5% improvement in F1 score and a 25% increase in Precision and Recall. DALL-M bridges an important gap in clinical data augmentation by preserving data integrity while enhancing predictive modeling in healthcare. Our results show that integrating LLM-generated synthetic features significantly improves model performance, making DALL-M a scalable and practical approach for AI-driven medical diagnostics.

The metaverse offers unprecedented opportunities for collaboration, yet its success hinges on rethinking how people perceive and interact in shared virtual spaces. This keynote explores how deliberate distortions of reality-through spatial manipulation, altered proxemics, and amplified cues can foster more effective communication, coordination, and creativity. Drawing on decades of work in computer graphics, extended reality, and human-computer interaction, I will illustrate how techniques inspired by performance arts, visual storytelling, and medical applications open new paradigms for collaborative interaction. By challenging realism and embracing controlled exaggeration, we can design metaverse experiences that are not only more engaging but also more supportive of human potential.

Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems. However, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest, highlighting a need for methods to effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach's real-time performance on real-world datasets. They show that it outperforms state-of-the-art denoising and restoration performance at over 30fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications.

Engaging professionals from all areas of computing, the IEEE Computer Society sets the standard for education and engagement that fuels global technological advancement.Through conferences, publications, and programs, IEEE CS empowers, guides, and shapes the future of its members, and the greater industry, enabling new opportunities to better serve our world.

This roundtable discussion, featuring four industry experts, examines the complexities surrounding artificial intelligence (AI)’s role in extended reality. Topics include ethical implications of data usage, compensation for content creators, and the need for transparency in AI decision-making.

Informal caregivers play an essential role in caring for persons who require assistance and in managing the health of their loved ones. Unfortunately, they need more health, leisure, and relaxation time. Nature interaction is one of many kinds of self-care intervention. It has long been regarded as a refreshing break from stressful routines, and research suggests exposure to nature interventions to improve the quality of life of caregivers. Despite not being the real thing, technology allows us alternatives that can still have some beneficial effects. In this preliminary study, we explore the benefits of natural environment videos on informal caregivers as an alternative to exposure to nature. Specifically, we are interested in the effects of their own choices versus a random video. We found that natural environment videos improve the well-being of informal caregivers in at least three key areas: valence, arousal, and negative affect. Furthermore, the effect increases when they choose the video they want to watch instead of a random video. This effect benefits the studied subjects because they need more time and energy to visit real natural environments.

The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in analyzing how much effort they need to put into the course to obtain the desired grades while helping course instructors identify student types at the early stages of the course to provide better support for them. Our method, initially clusters students into several groups based on experience points (XP) that they obtain during a semester. Then, we estimate cluster size and balance clusters by generating and adding virtual students to the smaller clusters. Finally, we drop unimportant student attributes using a feature selection technique. We then predict their final grades via three different algorithms. We have compared the performance of our method with other approaches using data collected from a course for nine years, using data collected from 679 students. The results indicate that our method outperformed the others while achieving 78.02% average accuracy only four weeks after starting the course. It shows we can effectively predict final grades, which will potentially enhance students’ learning outcomes.

Digital anatomy has emerged as an essential subfield of anatomy that processes the human body with the help of a computer. 3D reconstruction tools have been developed over the years, being always complementary to cadaver dissection. Teachers' main goal is to provide a greater understanding of the spatial structures of the body and its internal organs thanks to 3D reconstruction techniques. Manual segmentation and creation of 3D vectorial models is probably the best anatomy learning tool for beginners and students. The aim of this chapter is to show how to create vectorial models from anatomical slices with Winsurf® software, highlighting their educational value. Resulting 3D models could also be used through the interface of virtual dissection, which has been proven to be immensely valuable for anatomy education, particularly by using immersive reality techniques. It could also be the basis of future simulation tools for surgeon training.

Deep learning models have achieved high performance across different domains, such as medical decision making, autonomous vehicles, decision support systems, among many others. However, despite this success, the inner mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This opacity makes it hard to understand the how or the why of the predictions of deep learning models. There has been a growing interest in model-agnostic methods that make deep learning models more transparent and explainable to humans. Some researchers recently argued that for a machine to achieve human level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing literature on counterfactuals and causability for explainable artificial intelligence (AI). We performed a Latent Dirichlet topic modelling analysis (LDA) under a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to find the most relevant literature articles. This analysis yielded a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications to real-world data. Our research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Furthermore, our findings suggest that the explanations derived from popular algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. Thus, this paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable AI.

This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in five different datasets (COMPAS, Adult, German, Diabetes, and Breast Cancer). Our findings indicate that: (1) Different machine learning models have no impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation process. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A qualitative analysis is strongly recommended (together with a quantitative analysis) to ensure a robust analysis of counterfactual explanations and the potential identification of biases.

Anatomy studies the morphology and structure of organisms. The word originates from the Greek ana-, up; and tome-, meaning cutting. As its name implies, anatomy relies heavily on dissection and studies the human body parts’ arrangement and interaction. Heir from a rich Greco-Roman tradition and background, Vesalius () is arguably the father of modern anatomy (Fig. 1.1). Since its early origins, there has been a clear connection between the study of anatomy, graphics depictions, and illustrative visualizations. True to its origins, computer-based three-dimensional modeling of the human body, also known as Digital Anatomy, has strong visualization roots. Digital Anatomy has benefited from the computer and communications technological revolution. It lies at the intersection of converging different disciplines, ranging from Medical Imaging, Medical Visualization, 3D printing, and Computer Graphics to Artificial Intelligence and Robotics. This book offers a perspective on current developments and a road map into the future for this exciting pillar of modern medicine. Open image in new window Fig. 1.1 One of Andreas Vesalius most famous anatomical illustrations (Courtesy of the U.S. National Library of Medicine) published in (Vesalius )

Communication is a critical component of human interaction in order to satisfy basic human needs. This is especially true in technology-supported learning environments, where establishing multiple communicational channels between instructors and students is a requisite to enhance learning. Therefore, specific intervention strategies are required to effectively adjust each channel to satisfy students’ communication needs while learning. However, not all online communication channels achieve the goal of including students into their learning communities. Thus, designers should understand what works best in facilitating effective communication and learning. The bottom line lies in how to foster online communication and what impact it may have on effective learning. This paper aims at exploring student use of asynchronous and synchronous communication tools, namely fora and chat tools, and their participation within a technology-supported Multimedia course. We pre-sent ongoing research results related to this specific issue, together with implications for designing b-learning experiences, lessons learnt and future work. By this we aim at contributing to understand the impact of online communication tools on student behavior and learning results.