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Since late 2022, Large Language Models (LLMs) have become very prominent with LLMs like ChatGPT and Bard receiving millions of users. Hundreds of new LLMs are announced each week, many of which are deposited to Hugging Face, a repository of machine learning models and datasets. To date, nearly 16,000 Text Generation models have been uploaded to the site. Given the huge influx of LLMs, it is of interest to know which LLM backbones, settings, training methods, and families are popular or trending. However, there is no comprehensive index of LLMs available. We take advantage of the relatively systematic nomenclature of Hugging Face LLMs to perform hierarchical clustering and identify communities amongst LLMs using n-grams and term frequency-inverse document frequency. Our methods successfully identify families of LLMs and accurately cluster LLMs into meaningful subgroups. We present a public web application to navigate and explore Constellation, our atlas of 15,821 LLMs. Constellation rapidly generates a variety of visualizations, namely dendrograms, graphs, word clouds, and scatter plots. Constellation is available at the following link: https://constellation.sites.stanford.edu/., 14 pages, 6 figures, 1 table

New systems employ Machine Learning to sift through large knowledge sources, creating flexible Large Language Models. These models discern context and predict sequential information in various communication forms. Generative AI, leveraging Transformers, generates textual or visual outputs mimicking human responses. It proposes one or multiple contextually feasible solutions for a user to contemplate. However, generative AI does not currently support traceability of ideas, a useful feature provided by search engines indicating origin of information. The narrative style of generative AI has gained positive reception. People learn from stories. Yet, early ChatGPT efforts had difficulty with truth, reference, calculations, and aspects like accurate maps. Current capabilities of referencing locations and linking to apps seem to be better catered by the link-centric search methods we've used for two decades. Deploying truly believable solutions extends beyond simulating contextual relevance as done by generative AI. Combining the creativity of generative AI with the provenance of internet sources in hybrid scenarios could enhance internet usage. Generative AI, viewed as drafts, stimulates thinking, offering alternative ideas for final versions or actions. Scenarios for information requests are considered. We discuss how generative AI can boost idea generation by eliminating human bias. We also describe how search can verify facts, logic, and context. The user evaluates these generated ideas for selection and usage. This paper introduces a system for knowledge workers, Generate And Search Test, enabling individuals to efficiently create solutions previously requiring top collaborations of experts., 8 pages, 21 references

Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason -- particularly important for this line of research -- is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and 'calculating' with ordinal structures -- a specific class of directed graphs -- and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics., 33 pages

Online whiteboards are becoming a popular way to facilitate collaborative design work, providing a free-form environment to curate ideas. However, as templates are increasingly being used to scaffold contributions from non-experts designers, it is crucial to understand their impact on the creative process. In this paper, we present the results from a study with 114 students in a large introductory design course. Our results confirm prior findings that templates benefit students by providing a starting point, a shared process, and the ability to access their own work from previous steps. While prior research has criticized templates for being too rigid, we discovered that using templates within a free-form environment resulted in visual patterns of free-form curation where concepts were spatially organized, clustered, color-coded, and connected using arrows and lines. We introduce the concept of "Free-form Templates" to illustrate how templates and free-form curation can be synergistic.

The security and privacy of refugee communities have emerged as pressing concerns in the context of increasing global migration. The Rohingya refugees are a stateless Muslim minority group in Myanmar who were forced to flee their homes after conflict broke out, with many fleeing to neighbouring countries and ending up in refugee camps, such as in Bangladesh. However, others migrated to Malaysia and those who arrive there live within the community as urban refugees. However, the Rohingya in Malaysia are not legally recognized and have limited and restricted access to public resources such as healthcare and education. This means they face security and privacy challenges, different to other refugee groups, which are often compounded by this lack of recognition, social isolation and lack of access to vital resources. This paper discusses the implications of security and privacy of the Rohingya refugees, focusing on available and accessible technological assistance, uncovering the heightened need for a human-centered approach to design and implementation of solutions that factor in these requirements. Overall, the discussions and findings presented in this paper on the security and privacy of the Rohingya provides a valuable resource for researchers, practitioners and policymakers in the wider HCI community., 5 pages, 3 figures, CHI'23 Workshop on Migration, Security and Privacy (see https://migrationsecurityprivacy.uk)

Virtual reality (VR)-based immersive analysis has become an alternative to traditional approaches for analyzing complex, multidimensional human motion data. However, existing VR-based methods lack detailed information about hand motion and object interaction, which is essential for interpreting human activities and identifying their needs. To address that, we present a new VR system, VRMoVi, with a unique design of three expressive visualization layers: 1) a 3D tube layer for hand/object general motion, 2) a hand-object avatar layer for hand-object interaction animation, and 3) a particle-with-arrow layer for detailed hand positions and orientations. We validated VRMoVi with a real-world VR human motion dataset and conducted a user study with 24 participants. Compared with other visualization conditions, VRMoVi performed significantly better than the traditional 2D condition and slightly better than the standard VR-based condition; users found VRMoVi to be comprehensible, immersive, easy to use, and useful for interpreting human activity data., 10 pages, 5 figures, and 2 tables

Deepfakes are AI-synthesized content that are becoming popular on many social media platforms, meaning the use of deepfakes is increasing in society, regardless of its societal implications. Its implications are harmful if the moral intuitions behind deepfakes are problematic; thus, it is important to explore how the moral intuitions behind deepfakes unfold in communities at scale. However, understanding perceived moral viewpoints unfolding in digital contexts is challenging, due to the complexities in conversations. In this research, we demonstrate how Moral Foundations Theory (MFT) can be used as a lens through which to operationalize moral viewpoints in discussions about deepfakes on Reddit communities. Using the extended Moral Foundations Dictionary (eMFD), we measured the strengths of moral intuition (moral loading) behind 101,869 Reddit posts. We present the discussions that unfolded on Reddit in 2018 to 2022 wherein intuitions behind some posts were found to be morally questionable to society. Our results may help platforms detect and take action against immoral activities related to deepfakes., This is more related to CSCW, societal implications. Manuscript to be submitted to SM+S Journal (Sage Group)

Graphical perception studies typically measure visualization encoding effectiveness using the error of an "average observer", leading to canonical rankings of encodings for numerical attributes: e.g., position > area > angle > volume. Yet different people may vary in their ability to read different visualization types, leading to variance in this ranking across individuals not captured by population-level metrics using "average observer" models. One way we can bridge this gap is by recasting classic visual perception tasks as tools for assessing individual performance, in addition to overall visualization performance. In this paper we replicate and extend Cleveland and McGill's graphical comparison experiment using Bayesian multilevel regression, using these models to explore individual differences in visualization skill from multiple perspectives. The results from experiments and modeling indicate that some people show patterns of accuracy that credibly deviate from the canonical rankings of visualization effectiveness. We discuss implications of these findings, such as a need for new ways to communicate visualization effectiveness to designers, how patterns in individuals' responses may show systematic biases and strategies in visualization judgment, and how recasting classic visual perception tasks as tools for assessing individual performance may offer new ways to quantify aspects of visualization literacy. Experiment data, source code, and analysis scripts are available at the following repository: https://osf.io/8ub7t/?view\_only=9be4798797404a4397be3c6fc2a68cc0., 16 pages, 9 figures

Gardening is an activity that involves a number of dimensions of increasing interest to HCI and CSCW researchers, including recreation, sustainability, and engagement with nature. This paper considers the garden setting in order to understand the role that collaborative and social computing technologies might play for practitioners engaging in outdoor skilled activities. We conducted participant observations with nine experienced gardeners aged 22-71 years. Through this process, we find that gardeners continuously configure their environments to accommodate their preferences for sociality. They share embodied skills and help others attune to sensory information in person, but also influence learning through the features in their garden that are observed by others. This paper provides an understanding of sociality in the garden, highlights skill sharing as a key domain for design in this space, and contributes design considerations for collaborative technologies in outdoor settings., 13 pages

The language for expressing comparisons is often complex and nuanced, making supporting natural language-based visual comparison a non-trivial task. To better understand how people reason about comparisons in natural language, we explore a design space of utterances for comparing data entities. We identified different parameters of comparison utterances that indicate what is being compared (i.e., data variables and attributes) as well as how these parameters are specified (i.e., explicitly or implicitly). We conducted a user study with sixteen data visualization experts and non-experts to investigate how they designed visualizations for comparisons in our design space. Based on the rich set of visualization techniques observed, we extracted key design features from the visualizations and synthesized them into a subset of sixteen representative visualization designs. We then conducted a follow-up study to validate user preferences for the sixteen representative visualizations corresponding to utterances in our design space. Findings from these studies suggest guidelines and future directions for designing natural language interfaces and recommendation tools to better support natural language comparisons in visual analytics., 11 pages, 5 figures, 1 table, to appear in IEEE Transactions of Visualization and Computer Graphics (Also in proceedings of IEEE VIS 2022)

Trust is often cited as an essential criterion for the effective use and real-world deployment of AI. Researchers argue that AI should be more transparent to increase trust, making transparency one of the main goals of XAI. Nevertheless, empirical research on this topic is inconclusive regarding the effect of transparency on trust. An explanation for this ambiguity could be that trust is operationalized differently within XAI. In this position paper, we advocate for a clear distinction between behavioral (objective) measures of reliance and attitudinal (subjective) measures of trust. However, researchers sometimes appear to use behavioral measures when intending to capture trust, although attitudinal measures would be more appropriate. Based on past research, we emphasize that there are sound theoretical reasons to keep trust and reliance separate. Properly distinguishing these two concepts provides a more comprehensive understanding of how transparency affects trust and reliance, benefiting future XAI research., Will be published at the CHI 2022 Workshop on Trust and Reliance in AI-Human Teams. New Orleans '22: ACM Conference on Human Factors in Computing Systems

Existing approaches for embedding unobtrusive tags inside 3D objects require either complex fabrication or high-cost imaging equipment. We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects, and detected rapidly by low-cost near-infrared cameras. We achieve this by printing objects from an infrared-transmitting filament, which infrared cameras can see through, and by having air gaps inside for the tag's bits, which appear at a different intensity in the infrared image. We built a user interface that facilitates the integration of common tags (QR codes, ArUco markers) with the object geometry to make them 3D printable as InfraredTags. We also developed a low-cost infrared imaging module that augments existing mobile devices and decodes tags using our image processing pipeline. Our evaluation shows that the tags can be detected with little near-infrared illumination (0.2lux) and from distances as far as 250cm. We demonstrate how our method enables various applications, such as object tracking and embedding metadata for augmented reality and tangible interactions., 12 pages, 10 figures. To appear in the Proceedings of the 2022 ACM Conference on Human Factors in Computing Systems

Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks., 9 pages, 10 figures, 1 table. Accepted by IEEE VIS 2021

Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities., IEEE VIS (InfoVis/VAST/SciVis) 2022

We present how we have created a series of bilingual (English and Welsh) STEM activities focusing on rainbows, colours, light and optical effects. The activities were motivated by the many rainbows that appeared in windows in the UK, in support of the National Health Service at the start of the coronavirus pandemic. Rainbows are hopeful and are very fitting to be used as a positive iconic image at a time of much uncertainty. In this paper we explain how we have developed and organised the activities, focusing on colours, computer graphics and computer programming. Each lesson contains one or more activities, which enable people to take an active role in their learning. We have carefully prepared and organised several processes to guide academic colleagues to create and publish different activities in the theme. Which means that the activities appear similarly structured, can be categorised and searched in a consistent way. This structure can act as a blueprint for others to follow and apply to develop their own online course. The activities incrementally take people through learning about colour, rainbows, planning what to program, design and strategies to create colourful pictures using simple computer graphics principles based in processing.org., 9 pages, 10 figures, 1 table. Accepted for publication in Proceedings EG UK Computer Graphics & Visual Computing (2021)

Whether investigating research questions or designing systems, many researchers and designers need to engage users with their personal data. However, it is difficult to successfully design user-facing tools for interacting with personal data without first understanding what users want to do with their data. Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data. We present a new method that directly tackles this challenge: the data engagement interview. This interview method incorporates an analyst to provide real-time personal data analysis, granting interview participants the opportunity to directly engage with their data, and interviewers to observe and ask questions throughout this engagement. We describe the method's development through a case study with asthmatic participants, share insights and guidance from our experience, and report a broad set of insights from these interviews., 22 pages, 3 figures, submitted to The Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) December 2021

A key task in design work is grasping the client's implicit tastes. Designers often do this based on a set of examples from the client. However, recognizing a common pattern among many intertwining variables such as color, texture, and layout and synthesizing them into a composite preference can be challenging. In this paper, we leverage the pattern recognition capability of computational models to aid in this task. We offer a set of principles for computationally learning personal style. The principles are manifested in PseudoClient, a deep learning framework that learns a computational model for personal graphic design style from only a handful of examples. In several experiments, we found that PseudoClient achieves a 79.40% accuracy with only five positive and negative examples, outperforming several alternative methods. Finally, we discuss how PseudoClient can be utilized as a building block to support the development of future design applications.

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations., Comment: Accepted to ACM 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021)

This article addresses the possibility of supporting the construction of emotions in the participants of Interactive Digital Narratives (IDN) by means of colours. The article uses goal models for expressing protostories. The core of the article consists of the case study where two colour synes-thetes were asked to choose colours for eight emotions. Thereafter the same synesthetes were asked to choose colours for the emotions that support the attainment of goals in the Cinderella narrative, which serves as an example protostory. The article also discusses the perspectives for applying the method proposed by us to using colours in constructing emotions by IDNs.

Smartphones are now ubiquitous, yet our understanding of user interface factors that maximize mobile user experience (UX), is still limited. This work presents a controlled experiment, which investigated factors that affect the usability and UX of a mobile incident reporting app. The results indicate that sequence of user interface elements matters while striving to increase UX, and that there is no difference between tab and scrolling as navigation modalities in short forms. These findings can serve as building blocks for empirically-derived guidelines for mobile incident reporting., Paper accepted for presentation at the 5th International Conference on Human Computer Interaction Theory and Applications (HUCAPP@VISIGRAPP 2021)

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow we conducted multiple case studies with domain experts from different application areas and with different data modalities., Comment: 11 pages, 10 figures, 2 tables. Revised based on IEEE VIS 2020 reviewers comments. ACM 2012 CCS - Human-centered computing, Visualization, Visualization application domains, Visual analytics. Binary of the presented tool is available is our repository: https://doi.org/10.5281/zenodo.3885814

This paper discusses modern Auto Machine Learning (AutoML) tools from the perspective of a person with little prior experience in Machine Learning (ML). There are many AutoML tools both ready-to-use and under development, which are created to simplify and democratize usage of ML technologies in everyday life. Our position is that ML should be easy to use and available to a greater number of people. Prior research has identified the need for intuitive AutoML tools. This work seeks to understand how well AutoML tools have achieved that goal in practice. We evaluate three AutoML Tools to evaluate the end-user experience and system performance. We evaluate the tools by having them create models from a competition dataset on banking data. We report on their performance and the details of our experience. This process provides a unique understanding of the state of the art of AutoML tools. Finally, we use these experiences to inform a discussion on how future AutoML tools can improve the user experience for neophytes of Machine Learning., 10 pages, 3 tables, 3 figures. First author is a high school senior

Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a "human-in-the-loop" approach where domain experts use their knowledge to steer and adjust. However, the fragility, incompleteness, and opacity of these models means even minor changes could induce large and potentially undesirable changes in resulting model. In this paper we conduct a simulation-based analysis of human-centered interactions with topic models, with the objective of measuring the sensitivity of topic models to common classes of user actions. We find that user interactions have impacts that differ in magnitude but often negatively affect the quality of the resulting modelling in a way that can be difficult for the user to evaluate. We suggest the incorporation of sensitivity and "multiverse" analyses to topic model interfaces to surface and overcome these deficiencies., 16 Pages, 9 Figures, CHI 2021 Conference

In the last few years,Brain-Computer Interfaces (BCIs) have progressed as an emerging research area in the fields of human-computer interaction and interactive systems.This is primarily due to the introduction of low-cost electroencephalographic (EEG) systems that render BCI technology accessible for non-medical research but also due to the advancements of signal processing and machine learning methods.Consequently,BCIs could provide a wide new range of possibilities in the way users interact with a computer system (e.g., neuroadaptive interfaces).However,major challenges must still be addressed for BCI systems to mature into an established communication medium for effective human-computer interaction. One of the major challenges involves the easy integration of real-time processing pipelines with portable EEG systems for an out-of-the-lab use. To date, despite the amount of options current open-source tools provide, most toolboxes focus mainly in extending the processing and classification methods but lack on the ability to provide an easy-to-design yet extensible architecture for ubiquitous use.Here, we present NeuXus, a modular toolbox in Python for real-time biosignal processing and pipeline design.NeuXus is open-source and platform independent,providing high-level implementation of processing pipelines for easy BCI design and deployment., 12 pages, 2 figures, 1 table

This paper describes the design, implementation, and initial testing of a reusable platform for the creation of pervasive games with geo-localization services. We concentrate on role-playing games built by combining several types of simpler mini-games having three major components: Quests; Collectables; and Non-player characters (NPC). Quests encourage players to be active in their physical environment and take part in collaborative play; Collectables provide motivation; and NPCs enable player-friendly interaction with the platform. Each of these elements poses different technical requirements, which were met by implementing the gaming platform using the inTrack pervasive middle-ware being developed by our group. Several sample games were implemented and tested within the urban environment of Kyoto, Japan, using gaming clients running on mobile phones from NTT DoCoMo, Japan's largest mobile provider., 8 pages, 3 figures, International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2009

Survivors of central nervous system injury commonly present with spastic hypertonia. The affected muscles are hyperexcitable and can display involuntary static muscle tone and an exaggerated stretch reflex. These symptoms affect posture and disrupt activities of daily living. Symptoms are typically measured using subjective manual tests such as the Modified Ashworth Scale; however, more quantitative measures are necessary to evaluate potential treatments. The hands are one of the most common targets for intervention, but few investigators attempt to quantify symptoms of spastic hypertonia affecting the fingers. We present the isometric force pillow (IFP) to quantify involuntary grip force. This lightweight, computerized tool provides a holistic measure of finger flexion force and can be used in various orientations for clinical testing and to measure the impact of assistive devices.

Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. SpatialRugs apply 2D colormaps to visualize location mapped to a juxtaposed display. We explore the effect of various colormaps discussing perceptual limitations and introduce a custom color-smoothing method to mitigate distorted patterns of collective movement behavior., EuroVis Workshop on Visual Analytics (EuroVA), Visual Analytics Methods and Applications, 1, 5, Juri F. Buchmüller, Udo Schlegel, Eren Cakmak, Daniel A. Keim, and Evanthia Dimara, CCS Concepts: Human-centered computing --> Visualization techniques

We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs. We demonstrate its potential with a perfume bottle design case study, including human evaluation and quantitative and qualitative analyses., Thirty-third Conference on Neural Information Processing Systems (NeurIPS) 2019 Workshop on Machine Learning for Creativity and Design, December 14th, 2019, Vancouver, Canada (https://neurips2019creativity.github.io/)

It is developed the considerations from (S. M. Min\v{c}i\'c, [14, 15]) about curvature tensors and pseudotensors for a non-symmetric affine connection space in this paper. How many kinds of covariant derivatives are enough to be defined for complete researching in the field of non-symmetric affine connection spaces is examined here. This result is interpreted. After that, we searched how many curvature tensors and pseudotensors of a non-symmetric affine connection space are linearly independent. In the next, it is examined is it possible to express all curvature tensors as the linear combinations of the pseudotensors. At the end of this paper, we considered these results for possible applications in physics., Comment: 15 pages, 0 figures, paper from 14th International Conference on Geometry and Applications

The wide adoption of mobile devices and social media platforms have dramatically increased the collection and sharing of personal information. More and more frequently, users are called to take decisions concerning the disclosure of their personal information. In this study, we investigate the factors affecting users' choices toward the disclosure of their personal data, including not only their demographic and self-reported individual characteristics, but also their social interactions and their mobility patterns inferred from months of mobile phone data activity. We report the findings of a field-study conducted with a community of 63 subjects provided with (i) a smart-phone and (ii) a Personal Data Store (PDS) enabling them to control the disclosure of their data. We monitor the sharing behavior of our participants through the PDS, and evaluate the contribution of different factors affecting their disclosing choices of location and social interaction data. Our analysis shows that social interaction inferred by mobile phones is an important factor revealing willingness to share, regardless of the data type. In addition, we provide further insights on the individual traits relevant to the prediction of sharing behavior., 19 pages, 3 figures, 3 tables

In this position paper we propose a process model that provides a development infrastructure in which the usability engineering and software engineering life cycles co-exist in complementary roles. We describe the motivation, hurdles, rationale, arguments, and implementation plan for the need, specification, and the usefulness of such a model., This document contains 8 pages with 5 figures. This appeared in Bridging the SE & HCI Communities Workshop in INTERACT 2003 (http://www.se-hci.org/bridging/interact)