자연주의적 탐구조사(Naturalistic Inquiry)

IEEE Transactions on Pattern Analysis and Machine Intelligence

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.

http://ftp.misq.org/skin/frontend/default/misq/pdf/appendices/2011/BelangerCrosslerAppendices.pdf

Privacy in the digital age: a review of information privacy research in information systems

Choosing a suitable sample size in qualitative research is an area of conceptual debate and practical uncertainty. That sample size principles, guidelines and tools have been developed to enable researchers to set, and justify the acceptability of, their sample size is an indication that the issue constitutes an important marker of the quality of qualitative research. Nevertheless, research shows that sample size sufficiency reporting is often poor, if not absent, across a range of disciplinary fields. A systematic analysis of single-interview-per-participant designs within three health-related journals from the disciplines of psychology, sociology and medicine, over a 15-year period, was conducted to examine whether and how sample sizes were justified and how sample size was characterised and discussed by authors. Data pertinent to sample size were extracted and analysed using qualitative and quantitative analytic techniques. Our findings demonstrate that provision of sample size justifications in qualitative health research is limited; is not contingent on the number of interviews; and relates to the journal of publication. Defence of sample size was most frequently supported across all three journals with reference to the principle of saturation and to pragmatic considerations. Qualitative sample sizes were predominantly – and often without justification – characterised as insufficient (i.e., ‘small’) and discussed in the context of study limitations. Sample size insufficiency was seen to threaten the validity and generalizability of studies’ results, with the latter being frequently conceived in nomothetic terms. We recommend, firstly, that qualitative health researchers be more transparent about evaluations of their sample size sufficiency, situating these within broader and more encompassing assessments of data adequacy. Secondly, we invite researchers critically to consider how saturation parameters found in prior methodological studies and sample size community norms might best inform, and apply to, their own project and encourage that data adequacy is best appraised with reference to features that are intrinsic to the study at hand. Finally, those reviewing papers have a vital role in supporting and encouraging transparent study-specific reporting.

/pdf/characterising-and-justifying-sample-size-sufficiency-in-2hn81gqvva.pdf

Characterising and justifying sample size sufficiency in interview-based studies: systematic analysis of qualitative health research over a 15-year period

We describe the primary ways researchers can determine the size of a sample of research participants, present the benefits and drawbacks of each of those methods, and focus on improving one method that could be useful to the CHI community: local standards. To determine local standards for sample size within the CHI community, we conducted an analysis of all manuscripts published at CHI2014. We find that sample size for manuscripts published at CHI ranges from 1 -- 916,000 and the most common sample size is 12. We also find that sample size differs based on factors such as study setting and type of methodology employed. The outcome of this paper is an overview of the various ways sample size may be determined and an analysis of local standards for sample size within the CHI community. These contributions may be useful to researchers planning studies and reviewers evaluating the validity of results.

/pdf/local-standards-for-sample-size-at-chi-4ob283byv5.pdf

Local Standards for Sample Size at CHI

http://patientprivacyrights.org/wp-content/uploads/2013/01/Caine-Hanania-J2012.pdf

Patients want granular privacy control over health information in electronic medical records.

This new and completely updated edition is a comprehensive, easy-to-read, "how-to" guide on user research methods. You'll learn about many distinct user research methods and also pre- and post-method considerations such as recruiting, facilitating activities or moderating, negotiating with product developments teams/customers, and getting your results incorporated into the product. For each method, you'll understand how to prepare for and conduct the activity, as well as analyze and present the data - all in a practical and hands-on way. 

Each method presented provides different information about the users and their requirements (e.g., functional requirements, information architecture). The techniques can be used together to form a complete picture of the users' needs or they can be used separately throughout the product development lifecycle to address specific product questions. These techniques have helped product teams understand the value of user experience research by providing insight into how users behave and what they need to be successful. You will find brand new case studies from leaders in industry and academia that demonstrate each method in action. 

This book has something to offer whether you are new to user experience or a seasoned UX professional. After reading this book, you'll be able to choose the right user research method for your research question and conduct a user research study. Then, you will be able to apply your findings to your own products.



Completely new and revised edition includes 30+% new content!
Discover the foundation you need to prepare for any user research activity and ensure that the results are incorporated into your products 
Includes all new case studies for each method from leaders in industry and academia



Table of Contents


Preface

Part 1: What You Need to Know Before Choosing An Activity 1. Introduction to User Experience 2. Before You Choose an Activity: Learning About Your Product and Users 3. Ethical and Legal Considerations 4. Setting Up Research Facilities 5. Choosing a User Experience Research Activity

Part 2: Get Up and Running 6. Preparing For Your User Research Activity 7. During Your User Research Activity

Part 3: The Methods 8. Diary Studies 9. Interviews 10. Surveys 11. Card Sorting 12. Focus Groups 13. Field Studies 14. Evaluating Methods

Part 4: Wrapping Up 15. Concluding Final

Part 5: Appendices Appendix A. Requirements for Creating a Participant Recruitment Database Appendix B. Report Template Appendix C. Glossary Appendix D. References Index

Understanding Your Users: A Practical Guide to User Research Methods

Wearable devices have great potential to support several application domains ranging from medical and safety critical, to leisure and entertainment. Wearable devices’ solutions are promising, and extensive research has been conducted in this domain since the early 90’s. However most of these works focuses on the feasibility of individual solutions. As such, the human aspects are often neglected, which can decrease not only the acceptance levels for novel devices, but also their sustained engagement. To facilitate the consideration of human factors in the early design stage, we present and define a list of 20 human-centered design principles. We explain how each principle can be incorporated during the design phase of the wearable device creation process. By adopting these principles, we expect practitioners to achieve better wearable solutions, improving the user acceptance, satisfaction and engagement for novel applications.

Human Factors Considerations in the Design of Wearable Devices

In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the bone and tissue of the head. This audio data is then processed by a custom analog/digital circuit board. To ensure reliable (yet comfortable) contact between microphone and skin, all hardware components are incorporated into a 3D-printed behind-the-head framework. We collected field data with 14 participants for 32 hours in free-living conditions and additional eating data with 10 participants for 2 hours in a laboratory setting. We achieved accuracy exceeding 92.8% and F1 score exceeding 77.5% for eating detection. Moreover, Auracle successfully detected 20-24 eating episodes (depending on the metrics) out of 26 in free-living conditions. We demonstrate that our custom device could sense, process, and classify audio data in real time. Additionally, we estimate Auracle can last 28.1 hours with a 110 mAh battery while communicating its observations of eating behavior to a smartphone over Bluetooth.

/pdf/auracle-detecting-eating-episodes-with-an-ear-mounted-sensor-37rbjyayr5.pdf

Kelly Caine

Papers

Local Standards for Sample Size at CHI

Patients want granular privacy control over health information in electronic medical records.

Understanding Your Users: A Practical Guide to User Research Methods

Human Factors Considerations in the Design of Wearable Devices

Auracle: Detecting Eating Episodes with an Ear-mounted Sensor