Magic Center
Ph.D
Computing and Information Sciences
Room Chair - To be Announced
9:00 a.m. – Biru Cui
Title: Behavior prediction based on action history and social networks
Abstract: When a novel research topic emerges, we are interested in discovering how the topic
will propagate over the bibliography network, i.e., which author will research and publish about
this topic. Inferring the underlying influencenetwork among authors is the basis of predicting
such topic adoption. Existing works infer the influence network based on past adoption
cascades, which is limited by the amount and relevance of cascades collected. This work
hypothesizes that the influence network structure and probabilities are the results of many
factors including the social relationships and topic popularity. These heterogeneous
information shall be optimized to learn the parameters that define the homogeneous influence
network that can be used to predict future cascade. Experiments using DBLP data demonstrate
that the proposed method outperforms the algorithm based on traditional cascade network
inference in predicting novel topic adoption."
9:30 a.m. – Zach Fitzsimmons
Title: Single-Peaked Preferences and Partial Orders
Abstract: Single-peakedness is one of the most commonly examined domain restrictions on
preferences. We first provide a general overview of single-peaked preferences and their
implications in elections. We focus on algorithms and complexity results for the single-peaked
consistency problem, that is, determining if a given preference profile is single-peaked or not.
The preference profiles discussed are not restricted to total linear orders, we also look at partial
and weak preference orders and how we can decide the single-peaked consistency problem for
them. In addition, different notions of ""nearly"" single-peakedness, where a preference profile
is close to single-peaked with respect to some distance measure, are discussed.
10:00 a.m. – Erika Mesh
Title: Mapping the Chasm: Using Grounded Theory to Study Academic Scientific Software
Development Process Concerns
Abstract: Beginning in the early days of computing, higher-level programming languages (e.g.
FORTRAN) enabled scientists to tackle increasingly complex problems and data sets. Over time,
the technology and lessons learned from their efforts was applied to non-scientific problems to
enhance our everyday activities. As this shift occurred and software engineering (SE) evolved
into a standalone discipline, the needs and concerns of scientists became more distanced from
""mainstream"" SE research. This ""chasm"", as described by Kelly (2007), has resulted in many
scientific software development projects being left on their own to determine the applicability
of and approach for adopting SE best practices to improve their overall SE process. In order to
better understand this chasm and the SE process improvement (SPI) resources required to
bridge it, we have used an iterative grounded theory methodology to build a preliminary model
of the motivating concerns behind SPI decisions in a set of academic research projects.
10:30 a.m. – Xuan Guo
Title: Co-registration of Eye Tracking with EEG data for exploring humans' semantic
comprehension
Abstract: Eye tracking and electroencephalograph (EEG) has been used for unveiling human
visual perception and brain activities. We co-register these two data modalities to explore the
process of humans' comprehending semantics by analyzing their eye fixation related potentials
(FRPs) across different tasks/events.
11:00 a.m. – Sam Skalicky
Title: A Scheduling Approach to Processor Performance Modeling
Abstract: Large and complex systems are used more than ever before to solve the latest
compute-intensive problems that include an evolving combination of heterogeneous
processors. These processors operate on larger and larger data sizes, making performance
estimation difficult. The execution time of a computation can be estimated by scheduling the
work to the computational units of a processor. However this approach is limited by storage
and computational complexity requirements of the scheduling algorithm. In this paper we
present a new reduced graph representation to store very large graphs, and an algorithm for
estimating the length of the schedule. We prove that this algorithm is a 2-approximation
algorithm with a computational complexity on the order of the number of levels in the graph."
11:30 a.m. – Matthew Le
Title: Proving Determinism for a Parallel Functional Language with Speculation
Abstract: Parallel programming is often viewed as a daunting task for many, as programmers
are forced to reason about nondeterministic execution of programs and the exponential
number of interleavings of threads. Functional languages have shown to provide a nice solution
to these difficulties as they don’t allow mutation, effectively eliminating the possibility for
nondeterminism, making parallel programs easier to reason about. Unfortunately, a number of
applications can be more efficiently encoded when shared state is available. Recent efforts
have proposed forms of shared state that restrict the operations that can be performed, but
are able to guarantee deterministic execution. We build on these efforts by extending these
models with speculative parallelism. Additionally, we provide a rollback mechanism for
preserving determinism in the presence of cancelation and prove it’s correctness
Afternoon Session
Room Chair – Dr. Ray Ptucha
3:00 p.m. – Haitao Du
Title: Probabilistic Inference for Obfuscated Network Attack Sequences
Abstract: Facing diverse network attack strategies and overwhelming alters, much work has
been devoted to correlate observed malicious events to pre-defined scenarios, attempting to
deduce the attack plans based on expert models of how network attacks may transpire.
Sophisticated attackers can, however, employ a number of obfuscation techniques to confuse
the alert correlation engine or classifier. Recognizing the need for a systematic analysis of the
impact of attack obfuscation, our work models attack strategies as general finite order Markov
models and explicitly models obfuscated observations as noises. Taking into account that only
finite observation window and limited computational time can be afforded, this work develops
an algorithm to efficiently inference on the joint distribution of clean and obfuscated attack
sequences. The inference algorithm recovers the optimal match of obfuscated sequences to
attack models, and enables a systematic and quantitative analysis on the impact of obfuscation
on attack classification.
3:30 p.m. - Arthur Nunes-Harwitt
Title: On Deriving a PROLOG Compiler
Abstract: An interpreter is a concise definition of the semantics of a programming language and
is easily implemented. A compiler is more difficult to construct, but the code that it generates
runs faster than interpreted code. This research introduces rules to transform an interpreter
into a compiler. An extended example in the form of a PROLOG compiler suggests the utility of
the technique.
4:00 p.m. – Naseef Mansoor
Title: A Unified Design Methodology for Robust and Temperature-Aware Millimeter-wave
Wireless Network-on-Chip
Abstract: Network-on-Chip (NoC) paradigm has emerged as the interconnect fabric of multicore SoCs. The continuing demand for low power and high speed interconnects with technology
scaling necessitates looking beyond the conventional planar metal/dielectric-based
interconnect infrastructures. Among different possible alternatives, millimeter-wave (mmwave) wireless interconnects have emerged as a promising solution to the energy-latency
issues of global interconnects. Wireless Network-on-Chip (WiNoC) architectures utilizing these
CMOS compatible mm-wave transceivers can achieve significant improvements in performance
and energy-efficiency in on-chip data transfer for multi-core chips. A token-based medium
access mechanism is used in several mm-wave WiNoC architectures to enable a distributed and
optimal utilization of the available wireless bandwidth among multiple transmitters. However,
on-chip wireless interconnects being an emerging technology can suffer from high rates of
failures due to challenges in design and integration. Moreover, high frequency transceivers are
especially vulnerable to noise. Consequently, the token passing mechanism can fail and
significantly degrade the potential benefits of this novel interconnect technology. On the other
hand, excessive data-transfer over the wireless links to save energy consumption even causes
localized temperature hotspots in the NoC further aggravating the probability of failures. In this
work, we establish a unified robust and temperature-aware design methodology for WiNoC
architectures based on small-world networks. Through detailed system-level simulations and
real application based benchmarks we demonstrate that our design methodology of a smallworld mm-wave WiNoC architecture augmented with the token management unit (TMU) can
minimize the effect of failure of the wireless fabric as well as mitigate temperature hotspots in
WiNoC architectures.
4:30 p.m. – Srinivas Sridharan
Title: Collaborative Eye Tracking for Image Analysis
Abstract: We present a framework for collaborative image analysis where gaze information is
shared across all users. A server gathers and broadcasts fixation data from/to all clients and the
clients visualize this information. Several visualization options are provided. The system can run
in real-time or gaze information can be recorded and shared the next time an image is
accessed. Our framework is scalable to large numbers of clients with different eye tracking
devices. To evaluate our system we used it within the context of a spot-the-differences game.
Subjects were presented with 10 image pairs each containing 5 differences. They were given
one minute to detect the differences in each image. Our study was divided into three sessions.
In session 1, subjects completed the task individually, in session 2, pairs of subjects completed
the task without gaze sharing, and in session 3, pairs of subjects completed the task with gaze
sharing. We measured accuracy, time-to-completion and visual coverage over each image to
evaluate the performance of subjects in each session. We found that visualizing shared gaze
information by graying out previously scrutinized regions of an image significantly increases the
dwell time in the areas of the images that are relevant to the task (i.e. the regions where
differences actually occurred). Furthermore, accuracy and time-to-completion also improved
over collaboration without gaze sharing though the effects were not significant. Our framework
is useful for a wide range of image analysis applications which can benefit from a collaborative
approach.