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Modeling Security Policy and the Effect for End-Users
Many "good practices" in computer security are based on assumptions and local evidence that do not generalize. There are few quantifiable methods of establishing or refuting the validity of these practices from a user perspective. We propose a formal model of security policies that allows us to evaluate the claimed benefits to the user of the system quantitatively. We illustrate the use of the model by looking at a security policy we all live with daily: The Password Policy
Theory-based user modeling for personalized interactive information retrieval
In an effort to improve users’ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten users’ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.Comment: WSDM 201
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201
Developing and modelling complex social interventions: introducing the Connecting People Intervention
Objectives: Modeling the processes involved in complex social interventions is important in social work practice, as it facilitates their implementation and translation into different contexts. This article reports the process of developing and modeling the connecting people intervention (CPI), a model of practice that supports people with mental health problems to enhance their social networks.
Method: The CPI model was developed through an iterative process of focus group discussions with practitioners and service users and a two-stage Delphi consultation with relevant experts.
Results: We discuss the intervention model and the processes it articulates to provide an example of the benefits of intervention modeling.
Conclusions: Intervention modeling provides a visual representation of the process and outcomes of an intervention, which can assist practice development and lead to improved outcomes for service users
Simplifying knowledge creation and access for end-users on the SW
In this position paper, we argue that improved mechanisms for knowledge acquisition and access on the semantic web (SW) will be necessary before it will be adopted widely by end-users. In particular, we propose an investigation surrounding improved languages for knowledge exchange, better UI mechanisms for interaction, and potential help from user modeling to enable accurate, efficient, SW knowledge modeling for everyone
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