12 research outputs found
Team Formation for Scheduling Educational Material in Massive Online Classes
Whether teaching in a classroom or a Massive Online Open Course it is crucial
to present the material in a way that benefits the audience as a whole. We
identify two important tasks to solve towards this objective, 1 group students
so that they can maximally benefit from peer interaction and 2 find an optimal
schedule of the educational material for each group. Thus, in this paper, we
solve the problem of team formation and content scheduling for education. Given
a time frame d, a set of students S with their required need to learn different
activities T and given k as the number of desired groups, we study the problem
of finding k group of students. The goal is to teach students within time frame
d such that their potential for learning is maximized and find the best
schedule for each group. We show this problem to be NP-hard and develop a
polynomial algorithm for it. We show our algorithm to be effective both on
synthetic as well as a real data set. For our experiments, we use real data on
students' grades in a Computer Science department. As part of our contribution,
we release a semi-synthetic dataset that mimics the properties of the real
data
Using alloy to formally model and reason about an OpenFlow network switch
Openflow provides a standard interface for separating a network into a data plane and a programmatic control plane. This enables easy network reconfiguration, but introduces the potential for programming bugs to cause network effects. To study OpenFlow switch behavior, we used Alloy to create a software abstraction describing the internal state of a network and its OpenFlow switches. This work is an attempt to model the static and dynamic behaviour a network built using OpenFlow switches
A Team-Formation Algorithm for Faultline Minimization
In recent years, the proliferation of online resumes and the need to evaluate
large populations of candidates for on-site and virtual teams have led to a
growing interest in automated team-formation. Given a large pool of candidates,
the general problem requires the selection of a team of experts to complete a
given task. Surprisingly, while ongoing research has studied numerous
variations with different constraints, it has overlooked a factor with a
well-documented impact on team cohesion and performance: team faultlines.
Addressing this gap is challenging, as the available measures for faultlines in
existing teams cannot be efficiently applied to faultline optimization. In this
work, we meet this challenge with a new measure that can be efficiently used
for both faultline measurement and minimization. We then use the measure to
solve the problem of automatically partitioning a large population into
low-faultline teams. By introducing faultlines to the team-formation
literature, our work creates exciting opportunities for algorithmic work on
faultline optimization, as well as on work that combines and studies the
connection of faultlines with other influential team characteristics
Personalized Education; Solving a Group Formation and Scheduling Problem for Educational Content
ABSTRACT Wether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective; (1.) group students so that they can maximally benefit from peer interaction and (2.) find an optimal schedule of the educational material for each group. Thus, in this paper we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments we use real data on students' grades in a Computer Science department. As part of our contribution we release a semi-synthetic dataset that mimics the properties of the real data
1 Software-Defined IDS for Securing Embedded Mobile Devices
Abstract鈥擳he increasing deployment of networked mobile embedded devices leads to unique challenges communications security. This is especially true for embedded biomedical devices and robotic materials handling, in which subversion or denial of service could result in loss of human life and other catastrophic outcomes. In this paper we present the Learning Intrusion Detection System (L-IDS), a network security service for protecting embedded mobile devices within institutional boundaries, which can be deployed alongside existing security systems with no modifications to the embedded devices. L-IDS utilizes the OpenFlow Software-Defined Networking architecture, which allows it to both detect and respond to attacks as they happen. I
Using Alloy to Formally Model and Reason About an OpenFlow Network Switch
Abstract鈥擮penflow provides a standard interface for separating a network into a data plane and a programmatic control plane. This enables easy network reconfiguration, but introduces the potential for programming bugs to cause network effects. To study OpenFlow switch behavior, we used Alloy to create a software abstraction describing the internal state of a network and its OpenFlow switches. This work is an attempt to model the static and dynamic behaviour a network built using OpenFlow switches. I