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During the past two decades, information visualisation (InfoVis) research has created new techniques and methods to support data- intensive analyses in science, industry and government. These have enabled a wide range of analyses tasks to be executed, with tasks varying in terms of the type and volume of data involved. However, the majority of this research has focused on static datasets, and the analysis and visualisation tasks tend to be carried out by trained expert users. In more recent years, social changes and technological advances have meant that data have become more and more dynamic, and are consumed by a wider audience. Examples of such dynamic data streams include e-mails, status updates, RSS 1 feeds, versioning systems, social networks and others. These new types of data are used by populations that are not specifically trained in information visualization. Some of these people might consist of casual users, while others might consist of people deeply involved with the data, but in both cases, they would not have received formal training in information visualization. For simplicity, throughout this dissertation, I refer to the people (casual users, novices, data experts) who have not been trained in information visualisation as non-experts.These social and technological changes have given rise to multiple challenges because most existing visualisation models and techniques are intended for experts, and assume static datasets. Few studies have been conducted that explore these challenges. In this dissertation, with my collaborators, I address the question: Can we empower non-experts in their use of visualisation by enabling them to contribute to data stream analysis as well as to create their own visualizations?The first step to answering this question is to determine whether people who are not trained in information visualisation and the data sciences can conduct useful dynamic analysis tasks using a visualisation system that is adapted to support their tasks. In the first part of this dissertation I focus on several scenarios and systems where different sized crowds of InfoVis non-experts users (20 to 300 and 2 000 to 700 000 people) use dynamic information visualisation to analyse dynamic data.Another important issue is the lack of generic design principles for the visual encoding of dynamic visualization. In this dissertation I design, define and explore a design space to represent dynamic data for non-experts. This design space is structured by visual tokens representing data items that provide the constructive material for the assembly over time of different visualizations, from classic represen- tations to new ones. To date, research on visual encoding has been focused on static datasets for specific tasks, leaving generic dynamic approaches unexplored and unexploited.In this thesis, I propose construction as a design paradigm for non-experts to author simple and dynamic visualizations. This paradigm is inspired by well-established developmental psychological theory as well as past and existing practices of visualisation authoring with tangible elements. I describe the simple conceptual components and processes underlying this paradigm, making it easier for the human computer interaction community to study and support this process for a wide range of visualizations. Finally, I use this paradigm and tangible tokens to study if and how non-experts are able to create, discuss and update their own visualizations. This study allows us to refine our previous model and provide a first exploration into how non-experts perform a visual mapping without software. In summary, this thesis contributes to the understanding of dynamic visualisation for non-expert users. ; Durant les 20 dernières années, la recherche en visualisation d'informations (InfoVis) a permis l'émergence de nouvelles techniques et méthodes qui permettent d'assister l'analyse de données intensives pour la science, l'industrie, et les gouvernements. Cependant, la plupart de ces travaux de recherches furent orientés sur des données statiques pour des utilisateurs experts.Dernièrement, des évolutions technologique et sociétales ont eu pour effet de rendre les données de plus en plus dynamiques et accessibles pour une population plus diverse. Par exemple des flux de données tels que les emails, les mises à jours de statuts sur les réseaux sociaux, les flux RSS, les systèmes de gestion de versions, et bien d'autres. Ces nouveaux types de données sont utilisés par une population qui n'est pas forcément entraînée ou éduquée à utiliser des visualisations de données. La plupart de ces personnes sont des utilisateurs occasionnels, d'autres utilisent très souvent ces données dans leurs travaux. Dans les deux cas, il est probable que ces personnes n'aient pas reçu de formation formelle en visualisation de données.Ces changements technologiques et sociétaux ont généré une multitude de nouveaux défis, car la plupart des techniques de visualisations sont conçues pour des experts et des bases de données statiques. Peu d'études ont été conduites pour explorer ces défis. Dans ce rapport de thèse, j'adresse la question suivante : « Peut-­on permettre à des utilisateurs non­-experts de créer leur propre visualisation et de contribuer à l'analyse de flux de données ? »La première étape pour répondre à cette question est d'évaluer si des personnes non formées à la visualisation d'informations ou aux « data sciences » peuvent effectuer des tâches d'analyse de données dynamiques utiles, en utilisant un système de visualisation adapté pour supporter cette tâche. Dans la première partie de cette dissertation, je présente différents scénarios et systèmes, qui permettent à des utilisateurs non­-experts (de 20 à 300 ou 2000 à 700 000 personnes) d'utiliser la visualisation d'informations pour analyser des données dynamiques.Un autre problème important est le manque de principes génériques de design pour l'encodage visuel de visualisations d'informations dynamiques. Dans cette dissertation, je conçois, définis, et explore un espace de design pour représenter des donnés dynamiques pour des utilisateurs non­-experts. Cette espace de design est structuré par des jetons graphiques représentant des éléments de données qui permettent de construire dans le temps différentes visualisations, tant classiques que nouvelles.Dans cette thèse, je propose un nouveau paradigme de conception (design) pour faciliter la réalisation de visualisation d'informations par les utilisateurs non­-experts. Ce paradigme est inspiré par des théories établies en psychologie du développement, tout autant que par des pratiques passées et présentes de création de visualisation à partir d'objets tangibles. Je décris tout d'abord les composants et processus de bases qui structurent ce paradigme. Ensuite, j'utiliserai cette description pour étudier *si et comment* des utilisateur non­-experts sont capables de créer, discuter, et mettre à jour leurs propres visualisations. Cette étude nous permettra de réviser notre modèle précédent et de fournir une première exploration des phénomènes relatifs à la création d'encodages visuels par des utilisateurs non­-experts sans logiciel. En résumé, cette thèse contribue à la compréhension des visualisations dynamiques pour des utilisateurs non­-experts.

During the past two decades, information visualisation (InfoVis) research has created new techniques and methods to support data- intensive analyses in science, industry and government. These have enabled a wide range of analyses tasks to be executed, with tasks varying in terms of the type and volume of data involved. However, the majority of this research has focused on static datasets, and the analysis and visualisation tasks tend to be carried out by trained expert users. In more recent years, social changes and technological advances have meant that data have become more and more dynamic, and are consumed by a wider audience. Examples of such dynamic data streams include e-mails, status updates, RSS 1 feeds, versioning systems, social networks and others. These new types of data are used by populations that are not specifically trained in information visualization. Some of these people might consist of casual users, while others might consist of people deeply involved with the data, but in both cases, they would not have received formal training in information visualization. For simplicity, throughout this dissertation, I refer to the people (casual users, novices, data experts) who have not been trained in information visualisation as non-experts.These social and technological changes have given rise to multiple challenges because most existing visualisation models and techniques are intended for experts, and assume static datasets. Few studies have been conducted that explore these challenges. In this dissertation, with my collaborators, I address the question: Can we empower non-experts in their use of visualisation by enabling them to contribute to data stream analysis as well as to create their own visualizations?The first step to answering this question is to determine whether people who are not trained in information visualisation and the data sciences can conduct useful dynamic analysis tasks using a visualisation system that is adapted to support their tasks. In the first part of this dissertation ...

During the past two decades, information visualisation (InfoVis) research has created new techniques and methods to support data- intensive analyses in science, industry and government. These have enabled a wide range of analyses tasks to be executed, with tasks varying in terms of the type and volume of data involved. However, the majority of this research has focused on static datasets, and the analysis and visualisation tasks tend to be carried out by trained expert users. In more recent years, social changes and technological advances have meant that data have become more and more dynamic, and are consumed by a wider audience. Examples of such dynamic data streams include e-mails, status updates, RSS 1 feeds, versioning systems, social networks and others. These new types of data are used by populations that are not specifically trained in information visualization. Some of these people might consist of casual users, while others might consist of people deeply involved with the data, but in both cases, they would not have received formal training in information visualization. For simplicity, throughout this dissertation, I refer to the people (casual users, novices, data experts) who have not been trained in information visualisation as non-experts.These social and technological changes have given rise to multiple challenges because most existing visualisation models and techniques are intended for experts, and assume static datasets. Few studies have been conducted that explore these challenges. In this dissertation, with my collaborators, I address the question: Can we empower non-experts in their use of visualisation by enabling them to contribute to data stream analysis as well as to create their own visualizations?The first step to answering this question is to determine whether people who are not trained in information visualisation and the data sciences can conduct useful dynamic analysis tasks using a visualisation system that is adapted to support their tasks. In the first part of this dissertation ...

Motivated reasoning as a pervasive feature of human psychology poses challenges to the ideal of liberal democratic government, which relies on citizens' rationality. Motivated reasoning is at least partially caused by a biased store of knowledge, a partial set of accumulated information that skews reasoning about important political issues. However, there is some evidence that specialized training in a given domain may reduce the effects of motivated reasoning within that domain. To test whether a similar phenomenon is evident in the field of international relations, a signal detection technique is used to measure knowledge of U.S. foreign policy among two samples, one of IR professors and one of laypersons. The results uncover significant differences between experts and nonexperts, indicating that training in IR helps to reduce biases in knowledge, potentially providing "knowledge constraints" on motivated reasoning. Nonetheless, some evidence of bias among IR professors remains, suggesting that knowledge constraints on motivated reasoning may not fully allay normative concerns of bias in the domain of international relations.

Motivated reasoning as a pervasive feature of human psychology poses challenges to the ideal of liberal democratic government, which relies on citizens' rationality. Motivated reasoning is at least partially caused by a biased store of knowledge, a partial set of accumulated information that skews reasoning about important political issues. However, there is some evidence that specialized training in a given domain may reduce the effects of motivated reasoning within that domain. To test whether a similar phenomenon is evident in the field of international relations, a signal detection technique is used to measure knowledge of U.S. foreign policy among two samples, one of IR professors and one of laypersons. The results uncover significant differences between experts and nonexperts, indicating that training in IR helps to reduce biases in knowledge, potentially providing "knowledge constraints" on motivated reasoning. Nonetheless, some evidence of bias among IR professors remains, suggesting that knowledge constraints on motivated reasoning may not fully allay normative concerns of bias in the domain of international relations. ; peerReviewed ; publishedVersion

Forensic Accounting andFraud Investigation for NON-EXPERTSThird Edition The essential guide for every business professional, Forensic Accounting and Fraud Investigation for Non-Experts, Third Edition is a must-have resource if you've been wondering how financial fraud occurs in an organization and what to do if you find or suspect it. With comprehensive coverage, the authors present useful advice on where your organization is most susceptible to fraud. Forensic Accounting and Fraud Investigation for Non-Experts, Third Edition features: Coverage of the core accounting, investigative, and legal aspects of forensic accounting for professionals new to the field New cases and new material on technology tools in forensic accounting A look at the investigative and legal process along with interview techniques Highly readable and accessible, this timely book provides you with a complete grounding in all phases of forensic accounting and fraud investigation, with methods and tips that will enable you to readily recognize and investigate financial fraud, as well as the evolving sophistication of financial crimes occurring in every sector of industry. Executives, managers, criminal investigators, and prosecuting attorneys all require a basic understanding of accounting principles and investigative techniques to protect their organizations from fraud. Forensic Accounting and Fraud Investigation for Non-Experts, Third Edition helps non-accountants better understand basic forensic accounting principles, how different types of fraud occur, and how to detect and probe fraud in an approach that maximizes the chances of successful prosecution of the perpetrator

We introduce the problem of Task Assignment and Sequencing, which models online optimization in expert crowdsourcing settings that involve non-decomposable macrotasks. Non-decomposition is a property of certain types of complex problems, like the formulation of an R&D approach or the definition of a research methodology, which cannot be handled through the "divide-and-conquer" approach typically used in microtask crowdsourcing. In contrast to splitting the macrotask to multiple microtasks and allocating them to several workers in parallel, our model supports the sequential improvement of the macrotask one worker at a time, across distinct time slots of a given timeline, until a sufficient quality level is achieved. Our model assumes an online environment where expert workers are available only at specific time slots and worker/task arrivals are not known
a priori
. With respect to this setting, we propose TAS-ONLINE, an online algorithm that aims to complete as many tasks as possible within budget, required quality, and a given timeline, without any future input information regarding job release dates or worker availabilities. Experimental results comparing TAS-ONLINE to five benchmarks show that it achieves more completed jobs, lower flow times, and higher job quality. This work bears practical implications for providing performance and quality guarantees to expert crowdsourcing platforms that wish to integrate non-decomposable macrotasks into their offered services.

A Call for Papers for Panel on Economics and Language Use: The pragmatics of economics experts' engagement with non-specialists, 15th International Pragmatics Conference (IPrA2017) to be held in Belfast, Northern Ireland, 16-21 July 2017.

The dramatic increase in use of technological and algorithmic-based solutions for research, economic, and policy decisions has led to a number of high-profile ethical and privacy violations in the last decade. Current disparities in academic curriculum for data and computational science result in significant gaps regarding ethics training in the next generation of data-intensive researchers. Libraries are often called to fill the curricular gaps in data science training for non-data science disciplines, including within the University of California (UC) system. We found that in addition to incomplete computational training, ethics training is almost completely absent in the standard course curricula. In this report, we highlight the experiences of library data services providers in attempting to meet the need for additional training, by designing and running two workshops: Ethical Considerations in Data (2021) and its sequel Data Ethics & Justice (2022). We discuss our interdisciplinary workshop approach and our efforts to highlight resources that can be used by non-experts to engage productively with these topics. Finally, we report a set of recommendations for librarians and data science instructors to more easily incorporate data ethics concepts into curricular instruction.

The article presents the results of experimental research into psychophysiological and psycholinguistic features of perception and understanding of multimodal texts with extremist content. The hypothesis verification was implemented by means of the analysis of the respondents' eye movement data, involving forensic linguists, practicing in anti-extremism cases, well-informed and not well-informed non-experts. Research methods were eye-tracking, quantitative data processing, structural-semantic analysis. The following statistically reliable data were obtained: experts and poorly informed respondents spent more time on the task and had some similarities in the parameters of oculomotor activity, in contrast to well-informed respondents, who quickly completed the task, avoiding detailed analysis of the stimulus; the respondents who answered "not sure", spent more time on the task, their viewing strategy was characterized by long frequent fixations in comparison to other groups; respondents who gave benchmark answers recognized the elements of the image with longer sparse fixations, within the least amount of time. The longest time of viewing, number and duration of fixations were revealed for the signs that have the largest number of connections with the surrounding components; one of the highest values of viewing time is recorded for incomprehensible signs. The results of the study demonstrate the psycholinguistic and psychophysiological features of perception and understanding multimodal texts in forensic linguists compared to professionals in other fields. Being applicable to fundamental research into information perception and processing, the results can also contribute to working out the methods of multimodal text forensic linguistic analysis.