41,084 research outputs found
Authentication of Students and Studentsâ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11
Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are studentsâ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a studentâs work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio
Dynamic clamp with StdpC software
Dynamic clamp is a powerful method that allows the introduction of artificial electrical components into target cells to simulate ionic conductances and synaptic inputs. This method is based on a fast cycle of measuring the membrane potential of a cell, calculating the current of a desired simulated component using an appropriate model and injecting this current into the cell. Here we present a dynamic clamp protocol using free, fully integrated, open-source software (StdpC, for spike timing-dependent plasticity clamp). Use of this protocol does not require specialist hardware, costly commercial software, experience in real-time operating systems or a strong programming background. The software enables the configuration and operation of a wide range of complex and fully automated dynamic clamp experiments through an intuitive and powerful interface with a minimal initial lead time of a few hours. After initial configuration, experimental results can be generated within minutes of establishing cell recording
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
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 201
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