309,492 research outputs found
A “Learning Revolution”? Investigating Pedagogic Practices around Interactive Whiteboards in British Primary Classrooms
Interactive whiteboards have been rapidly introduced into all primary schools under UK Government initiatives. These large, touch-sensitive screens, which control a computer connected to a digital projector, seem to be the first type of educational technology particularly suited for whole-class teaching and learning. Strong claims are made for their value by manufacturers and policy makers, but there has been little research on how, if at all, they influence established pedagogic practices, communicative processes and educational goals. This study has been designed to examine this issue, using observations in primary (elementary) school classrooms. It is funded by the UK Economic and Social Research Council and builds on the authors’ previous research on ICT in educational dialogues and collaborative activities
Machine Learning Models that Remember Too Much
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and
services are available to data holders who are not ML experts but want to train
predictive models on their data. It is important that ML models trained on
sensitive inputs (e.g., personal images or documents) not leak too much
information about the training data.
We consider a malicious ML provider who supplies model-training code to the
data holder, does not observe the training, but then obtains white- or
black-box access to the resulting model. In this setting, we design and
implement practical algorithms, some of them very similar to standard ML
techniques such as regularization and data augmentation, that "memorize"
information about the training dataset in the model yet the model is as
accurate and predictive as a conventionally trained model. We then explain how
the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification
(CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20
Newsgroups and IMDB). In all cases, we show how our algorithms create models
that have high predictive power yet allow accurate extraction of subsets of
their training data
For Geometric Inference from Images, What Kind of Statistical Model Is Necessary?
In order to facilitate smooth communications with researchers in other fields including statistics, this paper investigates the meaning of "statistical methods" for geometric inference based on image feature points, We point out that statistical analysis does not make sense unless the underlying "statistical ensemble" is clearly defined. We trace back the origin of feature uncertainty to image processing operations for computer vision in general and discuss the implications of asymptotic analysis for performance evaluation in reference to "geometric fitting", "geometric model selection", the "geometric AIC", and the "geometric MDL". Referring to such statistical concepts as "nuisance parameters", the "Neyman-Scott problem", and "semiparametric models", we point out that simulation experiments for performance evaluation will lose meaning without carefully considering the assumptions involved and intended applications
Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection
Recent studies observe that app foreground is the most striking component
that influences the access control decisions in mobile platform, as users tend
to deny permission requests lacking visible evidence. However, none of the
existing permission models provides a systematic approach that can
automatically answer the question: Is the resource access indicated by app
foreground? In this work, we present the design, implementation, and evaluation
of COSMOS, a context-aware mediation system that bridges the semantic gap
between foreground interaction and background access, in order to protect
system integrity and user privacy. Specifically, COSMOS learns from a large set
of apps with similar functionalities and user interfaces to construct generic
models that detect the outliers at runtime. It can be further customized to
satisfy specific user privacy preference by continuously evolving with user
decisions. Experiments show that COSMOS achieves both high precision and high
recall in detecting malicious requests. We also demonstrate the effectiveness
of COSMOS in capturing specific user preferences using the decisions collected
from 24 users and illustrate that COSMOS can be easily deployed on smartphones
as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201
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