Non-additive interval-valued F-transform

International audienceThis article proposes a new interval-valued fuzzy transform. Its construction is based on a possibilistic interpretation of the partition on which the fuzzy transform is built. The main advantage of this approach is that it provides specific interval valued functions whose interpretation is straightforward. This interpretation relates to a traditional sampling/reconstruction framework where little is known about the sampling and/or reconstructing kernels. Numerous properties of the proposed approach are proved that could be useful for function analysis and comparison. In the experimental section, we illustrate some properties of the proposed transform while highlighting interesting features of the obtained framework

Imprecise linear filtering: a second step

International audienceLinear digital signal processing consists in convo- luting the input sampled signal with the discrete version of the impulse response of a filter designed by an expert. More than often, a unique impulse re- sponse does not represent the complete knowledge of the expert who should have proposed more than one appropriate filter. In a recent paper, we have proposed an extension of the finite impulse response filtering that able to represent the fact that the fil- ter is imprecisely known. This extension leads to compute an interval-valued filtered signal. In this paper, we propose a natural follow-up of this work by considering interval-valued input signals and re- placing the Choquet integral by the Šipoš integral

What User Behaviors Make the Differences During the Process of Visual Analytics?

The understanding of visual analytics process can benefit visualization researchers from multiple aspects, including improving visual designs and developing advanced interaction functions. However, the log files of user behaviors are still hard to analyze due to the complexity of sensemaking and our lack of knowledge on the related user behaviors. This work presents a study on a comprehensive data collection of user behaviors, and our analysis approach with time-series classification methods. We have chosen a classical visualization application, Covid-19 data analysis, with common analysis tasks covering geo-spatial, time-series and multi-attributes. Our user study collects user behaviors on a diverse set of visualization tasks with two comparable systems, desktop and immersive visualizations. We summarize the classification results with three time-series machine learning algorithms at two scales, and explore the influences of behavior features. Our results reveal that user behaviors can be distinguished during the process of visual analytics and there is a potentially strong association between the physical behaviors of users and the visualization tasks they perform. We also demonstrate the usage of our models by interpreting open sessions of visual analytics, which provides an automatic way to study sensemaking without tedious manual annotations.Comment: This version corrects the issues of previous version

eLuna : A Co-Design Framework for Mixed Reality Narrative Game- Based Learning

De siste tiårs utvidede fokus på læring utenfor skolen har bidratt til økt anvendelse av vitensentre som læringsarena for barn i grunnskole og videregående utdanning. En læringsløype er en type integrert læringsmiljø der de lærende, fysiske installasjoner, og digitale hjelpemidler bidrar til å fremme læringsinnhold og mål. På vitensentre brukes læringsløyper som pedagogisk støtte innen et bredt spekter av pensumplaner og programmer, gjennom å kombinere forskjellige sett av installasjoner og ved å vektlegge forskjellige aspekter av installasjonenes innhold. Siden de er sammensatt av både fysiske installasjoner og digitale hjelpemidler, er læringsløyper blandet virkelighet systemer, der de lærende interagerer med elementer i både den fysiske og virtuelle virkeligheten. Forskning har vist at både narrativ og spillmekanikker er blant de mest effektive komponentene som kan ligge til grunn for at læringsløyper skal kunne oppnå økt fokus på læringsinnhold, og for å engasjere de lærende ved å sette dem i en tilstand av flyt (av engelsk flow). Forskningen som presenteres i denne avhandlingen har som hovedmål å forbedre læring på vitensentre, gjennom å bidra med et co-design-rammeverk for blandet virkelighet narrative spillbaserte læringsløyper som underbygger positive effekter på engasjement, motivasjon, og læring. Narrativ har vært brukt til læring og instruksjon siden forhistorisk tid, og spill for læring har vært teoretisert og anvendt i mennesker i århundrer, i enda større grad etter oppfinnelsen av datamaskiner, og mulighetene bragt på banen gjennom digitale spill. Selv om bade narrative og spill har vært vist å kunne ha positive effekter når anvendt for læring, har forskning på effekter fra narrative spillbasert læring vist variable og motstridende resultater. Mangelen av en felles modell for kategorisering av narrative spill medfører manglende kunnskap relatert til hvordan og under hvilke forutsetninger narrative spill har effekt på læring. På tross av at de fleste studier av narrativ spillbasert læring unnlater å nevne narratologiske modeller, og de som gjør det primært refererer til modeller lånt fra andre media som mangler de nødvendige egenskapene til å kategorisere hendelsesflyten som benyttes i mange spill, finnes det en ludo narrativ variabel modell (LNVM), som er en narratologisk modell som kategorisere alle spill som narrativ. Denne forskningen videreutvikler LNVM, og presenterer en felles modell for kategorisering av narrativ spillbasert læring; eLNVM (fra engelsk: The extended LNVM). Narrative spillbaserte læringsløyper består av interaktive installasjoner og digitale hjelpemidler som belyser læringsmål innenfor pensumprogrammer. Det er derfor nødvendig med deltakelse både fra pedagoger og utviklere når slike læringsløyper skal designes og presenteres til lærende. Forskning viser at det er mangel av modeller, metoder, og rammeverk som myndiggjør pedagoger og utvikleres felles design av spillbasert læring, noe som enten resulterer i tapt fokus på læringsinnhold til fordel for engasjerende spillmekanikk, eller i at underholdningspotensialet i spill blir underordnet læringsmålene. Slike rammeverk må videre kunne skille mellom fysiske og virtuelle elementer for å være anvendbare i blandet virkelighet omgivelser. Forskningen presentert i denne avhandlingen benytter et rammeverk for informasjonssystemer som vitenskapelig metode til å utvikle eLuna co-design-rammeverket for blandet virkelighet narrative spillbaserte læringsløyper som underbygger positive effekter på engasjement, motivasjon, og læring. En systematisk litteraturstudie identifiserte 15 studier som rapporterte effekter fra digitale spillbaserte læringssystemer på engasjement, motivasjon, og læring. Disse systemene ble kategorisert med bruk av eLNVM og sortert basert på deres rapportering for å identifisere karakteristikker av narrative digital spillbasert læring som har positive effekter på engasjement, motivasjon, og læring. Denne forskningen benytter en iterativ design-basert forskningsprosess der karakteristikkene assosiert med de positive effektene legges til grunn for et co-design-rammeverk bestående av en metode og et visuelt språk. Co-design-rammeverket blir deretter utvidet med kapasitet til å separere mellom fysiske og virtuelle elementer i blandet virkelighet omgivelser. Rammeverket blir gjennom prosessen testet i deltakende co-design workshops og evaluert med bruk av varierte metoder, inkludert fokus grupper, intervjuer, spørreskjemaer, tematisk analyse, og heuristisk evaluering. Forskningen som blir presentert i denne doktoravhandlingen resulterer i eLuna co-design-rammeverket for narrative spillbasert læring, som kan bli brukt av pedagoger og utviklere til å lage både narrative digitale spillbaserte læringssystemer, og blandet virkelighet narrative spillbaserte læringsløyper som optimaliserer potensiale for positive effekter på engasjement, motivasjon, og læring.Increased focus on out of school learning over the last decades has led to extended use of science centres as learning arenas for pupils in primary and secondary education. A learning trail is a form of embedded learning environment in which the learners themselves, physical exhibits, and digital companions are elements that promote learning content and goals. When used in science centres, learning trails can combine different sets of exhibits and emphasize various aspects of their content to support learning goals inside a broad range of curricular plans and programs. Being comprised of physical exhibits and digital companions, science centre learning trails are mixed reality systems in which learner interaction occurs in both the physical and virtual domains. Research has shown that narratives and game mechanics are among the most effective components for science centre learning trails to achieve increased focus on the learning content, and to induce flow and engagement in learners. With an aim to contribute to improving science centre learning, the main objective of this research is to develop a co-design framework for mixed reality narrative game-based learning trails that enforce positive effects on engagement, motivation, and learning. Narratives have been used in learning and instruction since prehistoric times, and games for learning have been theorized and applied in human culture for centuries, increasingly so with the advent of the computer, and opportunities provided by digital games. While both narratives and games are shown to have the ability to positively affect learning, research on the effects from narrative game-based learning has shown mixed and contradictory results. The lack of a common model to categorize narrative games has led to a knowledge gap regarding how and under which conditions narrative games have effects on learning. Whereas most studies of narrative game-based learning neglect mentioning a narratological model at all, the ones that do mainly refer to models adapted from different media that lack the capabilities to properly categorize the event flow of many digital games. An exception is the ludo narrative variable model (LNVM), a narratological model that can properly categorize all games as narratives. Building on the LNVM, this research fills this gap with the development of the extended LNVM (eLNVM), a common model to categorize and isolate narratives in digital game-based learning. Narrative game-based learning trails comprise interactive exhibits and digital companions and promote learning goals inside curricular programs. Therefore, they require participation from educator and developer stakeholders to be properly designed and brought to learners. Research has shown that there is a lack of models, methods, or frameworks that empower educators and developers to co-design game-based learning, something which results in either the learning content being lost in the engaging mechanics of the game, or the fun of the games becoming inferior to the learning goals. Furthermore, to be applicable in science centres, such a co-design framework must also distinguish between physical and digital elements in mixed reality environments. Applying an information system research framework as a design science methodology, the eLuna co-design framework for mixed reality narrative game-based learning trails that enforce positive effects on engagement, motivation, and learning was developed. A systematic literature review identified 15 studies that self-reported effects of digital game-based learning systems on engagement, motivation, and learning. These were categorized on the eLNVM and sorted by their self-reported effects to identify what characterizes narrative digital game-based learning systems that positively affect engagement, motivation, and learning. Using an iterative design-based research process these characteristics associated with positive effects were then applied in a co-design framework comprising a method and a visual language, which was later extended with the capabilities to distinguish between physical and virtual elements in mixed reality learning trails. Throughout the process the framework was tested in co-design workshops with stakeholders and evaluated through mixed methods, including focus groups, semi-structured interviews, questionnaires, thematic analysis, and heuristic usability inspection. The research presented in this PhD dissertation contributes the eLuna co-design framework for narrative game-based learning, which empowers educators and developers in the creation of both narrative digital game-based learning and mixed reality narrative game-based learning trails that optimize the potential to induce positive effects on engagement, motivation, and learning.Doktorgradsavhandlin

Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. I continue to combine graph alignment features with lexical semantic similarity measures and employ machine learning techniques to show that grade assignment error can be reduced compared to a system that considers only lexical semantic measures of similarity. I also detail a preliminary attempt to align the dependency graphs of student and instructor answers in order to utilize a structural component that is necessary to simulate human-level grading of student answers. I further explore the utility of these techniques to several related tasks in natural language processing including the detection of text similarity, paraphrase, and textual entailment

Challenges in using cryptography - End-user and developer perspectives

"Encryption is hard for everyone" is a prominent result of the security and privacy research to date. Email users struggle to encrypt their email, and institutions fail to roll out secure communication via email. Messaging users fail to understand through which most secure channel to send their most sensitive messages, and developers struggle with implementing cryptography securely. To better understand how to support actors along the pipeline of developing, implementing, deploying, and using cryptography effectively, I leverage the human factor to understand their challenges and needs, as well as opportunities for support. To support research in better understanding developers, I created a tool to remotely conduct developer studies, specifically with the goal of better understanding the implementation of cryptography. The tool was successfully used for several published developers studies. To understand the institutional rollout of cryptography, I analyzed the email history of the past 27 years at Leibniz University Hannover and measured the usage of email encryption, finding that email encryption and signing is hardly used even in an institution with its own certificate authority. Furthermore, the usage of multiple email clients posed a significant challenge for users when using S/MIME and PGP. To better understand and support end users, I conducted several studies with different text disclosures, icons, and animations to find out if users can be convinced to communicate via their secure messengers instead of switching to insecure alternatives. I found that users notice texts and animations, but their security perception did not change much between texts and visuals, as long as any information about encryption is shown. In this dissertation, I investigated how to support researchers in conducting research with developers; I established that usability is one of the major factors in allowing developers to implement the functions of cryptographic libraries securely; I conducted the first large scale analysis of encrypted email, finding that, again, usability challenges can hamper adoption; finally, I established that the encryption of a channel can be effectively communicated to end users. In order to roll out secure use of cryptography to the masses, adoption needs to be usable on many levels. Developers need to be able to securely implement cryptography, and user communication needs to be either encrypted by default, and users need to be able to easily understand which communication' encryption protects them from whom. I hope that, with this dissertation, I show that, with supporting humans along the pipeline of cryptography, better security can be achieved for all

Opportunities and challenges in new survey data collection methods using apps and images.

Surveys are well established as an effective way of collecting social science data. However, they may lack the detail, or not measure the concepts, necessary to answer a wide array of social science questions. Supplementing survey data with data from other sources offer opportunities to overcome this. The use of mobile technologies offers many such new opportunities for data collection. New types of data might be able to be collected, or it may be possible to collect existing data types in new and innovative ways .As well as these new opportunities, there are new challenges. Again, these can both be unique to mobile data collection, or existing data collection challenges that are altered by using mobile devices to collect the data.The data used is from a study that makes use of an app for mobile devices to collect data about household spending, the Understanding Society Spending Study One. Participants were asked to report their spending by submitting a photo of a receipt, entering information about a purchase manually, or reporting that they had not spent anything that day. Each substantive chapter offers a piece of research exploring a different challenge posed by this particular research context. Chapter one explores the challenge presented by respondent burden in the context of mobile data collection. Chapter two considers the challenge of device effects. Chapter three examines the challenge of coding large volumes of organic data. The thesis concludes by reflecting on how the lessons learnt throughout might inform survey practice moving forward. Whilst this research focuses on one particular application it is hoped that this serves as a microcosm for contributing to the discussion of the wider opportunities and challenges faced by survey research as a field moving forward