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AI has become the poster child for a certain kind of thinking which holds that some technologies can become objective, independent and emergent entities which can evolve beyond the control of their creators. This thinking is not new however. It is a product of certain philosophical ideas such as materialism, a common-sense world of objective and independent objects, a correspondence theory of truth, and so forth, which are centered around the pre-eminence of science, epistemology, and logical reasoning, among others, as the supremely valid modes of engaging experience. This paper aims to critically examine the synthesis of the development program for AI to be found in the writings of computer scientist John McCarthy (1927–2011). McCarthy has been called one of the founding fathers of AI. First, some of the main themes which recur in his writings from as early as the mid nineteen fifties in the Dartmouth proposal, up to his late writings, are considered. A discussion of the goals that such a program implies, follows, in terms of society's relation with AI based technology and the nature and purpose of AI itself. These implications are arguably related to social control and are also moral. Ultimately, McCarthy's idea that AI is and can be built upon a paradigm of reasoning and logic so as to simulate a perfectly rational abstraction of human thinking is shown to be motivated by his view that AI systems should be controlled, as servants. This understanding of human thinking is questionable, however, given current research on human irrationality, as well as on the argumentative role of reasoning. And yet, the process of development toward the unachievable goal of controllable, rational, logical AI systems, has handily come to serve as a blind for increasing techno-corporate control of society, with techno-corporate interests displacing blame for devaluations and harms caused by their push to develop the technology to AI systems which they can conveniently 'lose control of.' [ABSTRACT FROM AUTHOR]

While a growing emphasis has been placed on broadening participation in computer science (CS) education, an enduring gender gap exists. One reason for this is gender-based CS stereotypes, which serve as gatekeepers and act in exclusionary ways. However, some high schools in the U.S. have still built gender-inclusive CS programs. We conducted a 3-month long case study at one such school (Forest View High School, FVHS, pseudonym) to better understand existing stereotypes. Data sources included student and alumnae interviews, student reflections, and teacher interviews. Constant comparative analysis was used to uncover emergent themes. Overall, gender-based stereotypes did not appear to be present, potentially as a result of mitigating strategies like access to women role models and recognition of the importance of a growth mindset. However, nerd-genius stereotypes were common. Despite a focus on nerd-genius stereotypes, students also held perceptions that computer scientists could be any type of person. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research. [ABSTRACT FROM AUTHOR]

Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon related to computers and consists primarily of man-made laws governing building, programming, and using computers. Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while the other sees computational thinking as the application of concepts from computer science to solve real-world problems. Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other than computer science before it can be declared a universal skill. [ABSTRACT FROM AUTHOR]

This Medical News article is an interview with University of Michigan computer scientist Jenna Wiens, whose research interests lie at the intersection of AI and health care, and JAMA Editor in Chief Kirsten Bibbins-Domingo. [ABSTRACT FROM AUTHOR]

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems. [ABSTRACT FROM AUTHOR]

Link prediction aims to anticipate the probability of a future connection between two nodes in a given network based on their previous interactions and the network structure. Link prediction is a rapidly evolving field of research that has attracted interest from physicists and computer scientists. Over the years, numerous methods have been developed for link prediction, encompassing similarity-based indices, machine learning techniques, and more. While existing surveys have covered link prediction research until 2020, there has been a substantial surge in research activities in recent years, particularly between 2021 and 2023. This increased interest underscores the pressing need to comprehensively explore the latest advancements and approaches in link prediction. We analyse and present the most notable research from 2018 to 2023. Our goal is to offer a comprehensive overview of the recent developments in the field. Besides summarizing and presenting previous experimental results, our survey offers a comprehensive analysis highlighting the strengths and limitations of various link prediction methods. [ABSTRACT FROM AUTHOR]

This article is an afterword to the book Rise of the Self-Replicators: Early Visions of Machines, AI and Robots That Can Reproduce and Evolve, coauthored by Tim Taylor and Alan Dorin (2020). The book covered the early history of thought about self-reproducing and evolving machines, from initial speculations in the 17th century up to the early 1960s (from which point onward the more recent history is already well covered elsewhere). This article supplements the material discussed in the book by presenting several relevant sources that have come to the author's attention since the book was published. The most significant additions to the history are from the German-born, 19th-century inventor and utopian John Adolphus Etzler in the 1830s–1840s, the Hungarian author and satirist Frigyes Karinthy in 1916, and the U.S. mathematician and computer scientist Fred Stahl in 1960. [ABSTRACT FROM AUTHOR]

Virtual reality is a rapidly growing technological innovation that continues to garner attention for use in education. This self-study of two teacher educators spans immersive head-mounted virtual reality implementation from 2014–2023 with middle-grades students, preservice teachers, and adult learners. Empirical data sources include learner perceptions and researcher generated teaching vignettes and reflective diaries. Framed by a systems thinking approach to design, results highlight the experiences and growing insights during each of the four chronologically ordered implementation phases. Phase one emphasizes the responsibility of project constituents and lack of organizational support. Phase two highlights the ripening of education virtual reality resources and adapting pedagogy to learner characteristics. Phase three explains the application and transfer within a new context. Phase four encapsulates shifts among external factors with a desire to improve performance needs. Findings compare and contrast education virtual reality with entertainment virtual reality, to advance instructional design and development to improve learning. Insights and practical recommendations apply to teacher educators, educational technologists, and computer scientists. [ABSTRACT FROM AUTHOR]

A key insight from research on organizational justice is that fairness is in the eye of the beholder. With increasing discussions – especially among computer scientists and policymakers – about the potential biases and unfairness of decisions made by Artificial Intelligence (AI) systems, there is a critical need to consider how decision-subjects perceive the fairness of AI-led decision-making. Drawing upon theoretical and empirical perspectives on perceived fairness in organizational justice scholarship, this review categorizes and analyzes perceptions of AI fairness as they impact the effective implementation of AI in workplaces and beyond. Specifically, we review existing empirical research on AI fairness according to distinct dimensions of perceived fairness – distributive, procedural, interpersonal, and informational – with a focus on its potential to inform organizational decision-making. In doing so, we provide new insights and offer directions for future interdisciplinary research in this burgeoning field. [ABSTRACT FROM AUTHOR]

In this Medical News article, Johns Hopkins University computer scientist Suchi Saria, PhD, MSc, discusses the use of AI tools in early sepsis detection and other clinical applications. [ABSTRACT FROM AUTHOR]

This paper explores the use of big data in understanding population movements in the context of environmental change, with particular attention to publicly available information from social media and newspapers. After discussing causal factors, the paper discusses the benefits and challenges of using big data, and mathematical models that may help capture the complexity of human mobility in the context of environmental change. We conclude that big data can be an important part of a toolkit in discerning drivers of migration and displacement. It is no panacea, however; the paper discusses potential problems in its use, particularly if big data are analysed to prevent people from moving out of harm's way. Therefore, partnerships between computer scientists and subject matter experts and practitioners are essential to ensuring that what is gathered from big data is usable, credible, and protects the privacy and rights of those who may be displaced. [ABSTRACT FROM AUTHOR]

In this paper we study the problem of the Malicious Maitre d', as described in Peter Winkler's book Mathematical Puzzles: A Connoisseur's Collection. This problem, attributed to computer scientist Rob Pike, involves seating diners around a circular table with napkins placed between each pair of adjacent settings. The goal of the maitre d' is to seat the diners in a way that maximizes the number of diners who arrive at the table to find the napkins on both the left and right of their place already taken by their neighbors. Winkler proposes a solution to the problem that he claims is optimal. We analyze Winkler's solution using tools from enumerative combinatorics, then present a new strategy that performs better. [ABSTRACT FROM AUTHOR]

Photographic images taken by tourists and uploaded to the African Carnivore Wildbook have been used by Cozzi et al. to identify individual African wild dogs and study their dispersal behavior. Collaborations among citizen scientists, computer scientists, and researchers can expand the reach of conservation efforts spatially and temporally. [ABSTRACT FROM AUTHOR]

Diversity has become increasingly relevant in computer science and technology development, both in terms of inclusive design teams and as a concept used to design "diversity-aware" technologies. The latter part has received less attention and notions of diversity leveraged to design technologies remain understudied. This paper critically examines diversity concepts employed by computer scientists and designers by reviewing 120 articles in two subfields of computer science: personalized recommendations and human–computer interaction. Drawing on Black feminism and critical race theory-inspired literature, I offer a critique of demographic and cultural user representations and underscore the shortcomings of diversity as a strategy to increase user satisfaction and fairness. Particularly concerning is the unreflected use of binary, static, and individual-level diversity concepts in the design of technologies. Such concepts render structural inequalities between and within groups of users invisible and thereby risk reinforcing existing injustices. The paper closes with considerations on whether and how diversity concepts can be leveraged by designers in a social justice–oriented manner. [ABSTRACT FROM AUTHOR]

"Anthropology of the Artificial" is a special section of articles by a collective of anthropologists, sociologists, digital media artists, and computer scientists that address the ever increasing encounters between humans and new kinds of machine systems. The contributions draw on a series of artistic and scientific research programs which call for new ways of thinking about the relations between human and artificial systems, focusing on anthropological encounters with artworks, software/hardware, and the public encounters with such systems in settings ranging from research labs to museums. [ABSTRACT FROM AUTHOR]

More intellectual modesty, but also conceptual clarity is urgently needed in AI, perhaps more than in many other disciplines. AI research has been coined by hypes and hubris since its early beginnings in the 1950s. For instance, the Nobel laureate Herbert Simon predicted after his participation in the Dartmouth workshop that "machines will be capable, within 20 years, of doing any work that a man can do". And expectations are in some circles still high to overblown today. This paper addresses the demand for conceptual clarity and introduces precise definitions of "strong AI", "superintelligence", the "technological singularity", and "artificial general intelligence" which ground in the work by the computer scientist Judea Pearl and the psychologist Howard Gardner. These clarifications allow us to embed famous arguments from the philosophy of AI in a more analytic context. [ABSTRACT FROM AUTHOR]

Despite being in a digital age, the diversity of young people choosing to study and work in computing is of ongoing concern, especially low numbers of girls. This paper aims to determine the profile of students who are more likely to aspire to be computer scientists and provide insights into the key factors that shape their aspirations in this area. We draw on questionnaire data from 4,983 secondary school students in England, in schools that have a higher-than-average uptake of GCSE Computer Science (a national exam taken by students at age 16). Amongst students who have chosen to study GCSE Computer Science, girls have a lower odds of aspiring to be a Computer Scientist compared to boys. Amongst younger students yet to pick their GCSE options, there are significantly more girls than boys wanting to work in digital art. Multivariable regression analysis identified 10 factors that were associated with aspirations to become a computer scientist. These include being a boy, being of Asian ethnicity, currently studying GCSE Computer Science, having a parent in computing, as well as having higher 'coding self-belief', elevated levels of 'family support' and aspiring towards 'technical jobs'. Implications for practice and curriculum design are discussed. [ABSTRACT FROM AUTHOR]

Healthcare has seen many advances in sensor technology, but with recent improvements in networks and the addition of the Internet of Things, it is even more promising. Current solutions to managing healthcare data with cloud computing may be unreliable at the most critical moments. High response latency, large volumes of data, and security are the main issues of this approach. The promising solution is fog computing, which offers an immediate response resistant to disconnections and ways to process big data using real-time analytics and artificial intelligence (AI). However, fog computing has not yet matured and there are still many challenges. This article presents for a computer scientist a systematic review of the literature on fog computing in healthcare. Articles published in six years are analysed from the service, software, hardware, information technologies and mobility with autonomy perspectives. The contribution of this study includes an analysis of recent trends, focus areas and benefits of the use of AI techniques in fog computing e-health applications. [ABSTRACT FROM AUTHOR]

More than 100 million individuals have been infected by the COVID19 virus since 2019. Even if the vaccination procedure has already begun, it will take time to attain an adequate supply. There have been several efforts by computer scientists to filter COVID19 from CXR or CT scans due to the disease's extensive prevalence. These patients' CT and CXR scans are utilized to identify COVID19 using IsoCovNet, a Graph‐Isomorphic‐Network, that is, GIN‐based model for detecting COVID19. A GIN‐based design dictates that our suggested model only takes data in the form of graphs. At the outset, the input image undergoes a conversion into an unordered network, that is, a graph that considers only the links between elements rather than the entire image. This approach significantly reduces the model's processing time. We verified the effectiveness of our proposed IsoCovNet network by using four datasets, which consist of both x‐ray and CT‐scan images, from five standard sources that are publicly available on platforms like Kaggle and GitHub. The network achieved an accuracy of 99.51% on binary datasets and a higher accuracy of 99.84% on the multi‐classification task of detecting Covid19. [ABSTRACT FROM AUTHOR]

Prophets of Computing: Visions of Society Transformed by Computing Edited by Dick van Lente. This book is a collection of fourteen chapters that discuss how societies in thirteen countries anticipated and thought of the role of computing since the end of World War II. [Extracted from the article]

This article connects recent work in formal epistemology to work in economics and computer science. Analysing the Dutch Book Arguments, Epistemic Utility Theory and Objective Bayesian Epistemology we discover that formal epistemologists employ the same argument structure as economists and computer scientists. Since similar approaches often have similar problems and have shared solutions, opportunities for cross-fertilisation abound. [ABSTRACT FROM AUTHOR]

The 2012 Menlo Report was an effort in which a group of computer scientists, US government funders, and lawyers produced ethics guidelines for research in information and communications technology (ICT). Here we study Menlo as a case of what we call ethics governance in the making, finding that this process examines past controversies and enrols existing networks to connect the everyday practice of ethics with ethics as a form of governance. To create the Menlo Report, authors and funders relied on bricolage work with existing, available resources, which significantly shaped both the report's contents and impacts. Report authors were motivated by both forward- and backward-looking goals: enabling new data-sharing as well as addressing past controversies and their implications for the field's body of research. Authors also grappled with uncertainty about which ethical frameworks were appropriate and made the decision to classify much network data as human subjects data. Finally, the Menlo Report authors attempted to enrol multiple existing networks in governance through appeals to local research communities as well as taking steps towards federal rulemaking. The Menlo Report serves as a case study in how to study ethics governance in the making: with attention to resources, adaptation, and bricolage, and with a focus on both the uncertainties the process tries to repair, as well as the new uncertainties the process uncovers, which will become the site of future ethics work. [ABSTRACT FROM AUTHOR]

Evolutionary game theory is a truly interdisciplinary subject that goes well beyond the limits of biology. Mathematical minds get hooked up in simple models for evolution and often gradually move into other parts of evolutionary biology or ecology. Social scientists realize how much they can learn from evolutionary thinking and gradually transfer insight that was originally generated in biology. Computer scientists can use their algorithms to explore a new field where machines not only learn from the environment, but also from each other. The breadth of the field and the focus on a few very popular issues, such as cooperation, comes at a price: several insights are re-discovered in different fields under different labels with different heroes and modelling traditions. For example, reciprocity or spatial structure are treated differently. Will we continue to develop things in parallel? Or can we converge to a single set of ideas, a single tradition and eventually a single software repository? Or will these fields continue to cross-fertilize each other, learning from each other and engaging in a constructive exchange between fields? Ultimately, the popularity of evolutionary game theory rests not only on its explanatory power, but also on the intuitive character of its models. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'. [ABSTRACT FROM AUTHOR]

Holonomic sequences are widely studied as many objects interesting to mathematicians and computer scientists are in this class. In the univariate case, these are the sequences satisfying linear recurrences with polynomial coefficients and also referred to as D -finite sequences. A subclass are C -finite sequences satisfying a linear recurrence with constant coefficients. We investigate the set of sequences which satisfy linear recurrence equations with coefficients that are C -finite sequences and call them C 2 -finite sequences. These sequences are a natural generalization of holonomic sequences. In this paper, we show that C 2 -finite sequences form a difference ring and provide methods to compute in this ring. Furthermore, we provide an analogous construction for D 2 -finite sequences, i.e., sequences satisfying a linear recurrence with holonomic coefficients. We show that these constructions can be iterated and obtain an increasing chain of difference rings. [ABSTRACT FROM AUTHOR]

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts. [ABSTRACT FROM AUTHOR]

The existence of factor and fractional factor in network graph in various settings has raised much attention from both mathematicians and computer scientists. It implies the availability of data transmission and network segmentation in certain special settings. In our paper, we consider P ≥ 2 -factor and P ≥ 3 -factor which are two special cases of general H -factor. Specifically, we study the existence of these two kinds of path factor when some subgraphs are forbidden, and several conclusions on the factor-deleted graph, factor critical-covered graph, and factor uniform graph are given with regards to network parameters. Furthermore, we show that these bounds are best in some sense. [ABSTRACT FROM AUTHOR]

Context: In this discussion, we began building two model, S2O + CHCl•− and O3 + CHCl•−, using DFT-BHandHLYP method, to study their reactions mechanisms on singlet PES. For this purpose, we hope to explore the effects of the difference between sulfur and oxygen atoms on the CHCl•− anion. Experimentalists and computer scientists may utilize the collected data to generate a wide range of hypotheses for experimental phenomena and predictions, allowing them to realize their full potential. Methods: The ion-molecule reaction mechanism of CHCl•− with S2O and O3 was studied using the DFT-BHandHLYP level of theory with the aug-cc-pVDZ basis set. Our theoretical findings show that Path 6 is the favored reaction pathway for CHCl•− + O3 reaction as identified by the O-abstraction reaction pattern. Comparing to the direct H- and Cl-abstraction mechanisms, the reaction (CHCl•− + S2O) prefers the intramolecular SN2 reaction pattern. Moreover, the calculated results demonstrated that the CHCl•− + S2O reaction is thermodynamically more favorable than the CHCl•− + O3 reaction, which is kinetically more advantageous. As a result, if the required reaction condition in the atmospheric process is met, the O3 reaction will happen more effectively. In terms of kinetics and thermodynamics viewpoints, the CHCl•− anion was very effective in eliminating S2O and O3. [ABSTRACT FROM AUTHOR]

Research on Artificial Intelligence, especially in the field of machine learning, has exploded in the twenty-first century. AI research in universities has long been funded by a combination of government and corporate sources. The funding of AI research in the contemporary university includes technology companies as both funders and generators of research areas. This paper looks at the links between technology companies and AI research in three areas: first, the ways in which technology companies influence both the content and practices of AI research in universities; second, how university research policies enable conditions that blur traditional boundaries between corporate and academic AI research; and third, how an ethos of 'open science', that is increasingly corporatised, moves ideas about AI from universities to companies. We conclude that technology companies influence AI research within established feedback loops in the transformed relationships between economy, society, research, and the contemporary university. [ABSTRACT FROM AUTHOR]

This article aims to investigate how participatory design influences interdisciplinary relationships in digital heritage projects. In particular, the article reflects on opportunities and challenges of interdisciplinary encounters in participatory design, with the cross-border project "Terra Mosana" as a case study, which aims to investigate, digitalize, and communicate the shared heritage of the Meuse–Rhine Euregion (EMR). Terra Mosana is a collaboration between multiple partners from municipalities, museums, cultural heritage sites, and universities in different EMR cities. Partners of the project have different backgrounds that vary from archaeologists, historians, and heritage professionals to computer scientists, developers, and communication specialists. My role in this project focused on designing and organizing several participatory design workshops with citizens of the EMR, aiming to empower and enable them to share their views about their shared history, and what they want from museums and other cultural institutions. Those workshops play a crucial role in the project by creating meaningful connections across the different disciplines involved in the project. In this article, I focus on the negotiation processes between the partners involved: What challenges were they confronted with? How did they arrive at creative solutions, and which issues remained unresolved? My analysis does not draw only on my participatory observation of workshops, but also on a focus group discussion that invited the partners to reflect on and assess the collaborative process. [ABSTRACT FROM AUTHOR]

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof. Many studies showed that ML models using anonymization techniques are vulnerable to various privacy attacks willing to expose sensitive information. As a privacy-preserving machine learning (PPML) technique that protects private data with sensitive information in ML, we propose a new task-specific adaptive differential privacy (DP) technique for structured data. The main idea of the proposed DP method is to adaptively calibrate the amount and distribution of random noise applied to each attribute according to the feature importance for the specific tasks of ML models and different types of data. From experimental results under various datasets, tasks of ML models, different DP mechanisms, and so on, we evaluate the effectiveness of the proposed task-specific adaptive DP method. Thus, we show that the proposed task-specific adaptive DP technique satisfies the model-agnostic property to be applied to a wide range of ML tasks and various types of data while resolving the privacy–utility trade-off problem. [ABSTRACT FROM AUTHOR]

Cognitive scientists, social psychologists, computer scientists, neuroscientists, ethologists and many others have all wondered how brains detect and interpret the motion of living organisms. It appears that specific cues, incorporated into our brains by natural selection, serve to signal the presence of living organisms. A simple geometric figure such as a triangle put in motion with specific kinematic rules can look alive, and it can even seem to have intentions and goals. In this article, we survey decades of parallel investigations on the motion cues that drive animacy perception—the sensation that something is alive—in non-human animals, especially in precocial species, such as the domestic chick, to identify inborn biological predispositions. At the same time, we highlight the relevance of these studies for an understanding of human typical and atypical cognitive development. [ABSTRACT FROM AUTHOR]

This paper presents contributions from using remote collaborative sketching to infovis for public health. The results come from an interdisciplinary study that involved designers, health researchers, public managers and computer scientists in the production of sketches generating alternatives for creating valuable graphics and dashboards. One of the creative stages was the remote collaborative workshops in which design alternatives were proposed based on sketches. The sketches were developed with the support of videoconferencing meetings, virtual boards, and graphic tablets. From this practice, we were able to include as contributions to the creative process in infovis: remote collaboration expansion; agile and iterative cycles improvement; interface design preview; data modeling supporting ; addressing graphic literacy issues; creative process documentation; participant experience improvement; and additional information incorporation. [ABSTRACT FROM AUTHOR]