32,770 research outputs found
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification
Videos are inherently multimodal. This paper studies the problem of how to
fully exploit the abundant multimodal clues for improved video categorization.
We introduce a hybrid deep learning framework that integrates useful clues from
multiple modalities, including static spatial appearance information, motion
patterns within a short time window, audio information as well as long-range
temporal dynamics. More specifically, we utilize three Convolutional Neural
Networks (CNNs) operating on appearance, motion and audio signals to extract
their corresponding features. We then employ a feature fusion network to derive
a unified representation with an aim to capture the relationships among
features. Furthermore, to exploit the long-range temporal dynamics in videos,
we apply two Long Short Term Memory networks with extracted appearance and
motion features as inputs. Finally, we also propose to refine the prediction
scores by leveraging contextual relationships among video semantics. The hybrid
deep learning framework is able to exploit a comprehensive set of multimodal
features for video classification. Through an extensive set of experiments, we
demonstrate that (1) LSTM networks which model sequences in an explicitly
recurrent manner are highly complementary with CNN models; (2) the feature
fusion network which produces a fused representation through modeling feature
relationships outperforms alternative fusion strategies; (3) the semantic
context of video classes can help further refine the predictions for improved
performance. Experimental results on two challenging benchmarks, the UCF-101
and the Columbia Consumer Videos (CCV), provide strong quantitative evidence
that our framework achieves promising results: on the UCF-101 and
on the CCV, outperforming competing methods with clear margins
Application Level High Speed Transfer Optimization Based on Historical Analysis and Real-time Tuning
Data-intensive scientific and commercial applications increasingly require
frequent movement of large datasets from one site to the other(s). Despite
growing network capacities, these data movements rarely achieve the promised
data transfer rates of the underlying physical network due to poorly tuned data
transfer protocols. Accurately and efficiently tuning the data transfer
protocol parameters in a dynamically changing network environment is a major
challenge and remains as an open research problem. In this paper, we present
predictive end-to-end data transfer optimization algorithms based on historical
data analysis and real-time background traffic probing, dubbed HARP. Most of
the previous work in this area are solely based on real time network probing
which results either in an excessive sampling overhead or fails to accurately
predict the optimal transfer parameters. Combining historical data analysis
with real time sampling enables our algorithms to tune the application level
data transfer parameters accurately and efficiently to achieve close-to-optimal
end-to-end data transfer throughput with very low overhead. Our experimental
analysis over a variety of network settings shows that HARP outperforms
existing solutions by up to 50% in terms of the achieved throughput
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey
Analyzing short texts infers discriminative and coherent latent topics that
is a critical and fundamental task since many real-world applications require
semantic understanding of short texts. Traditional long text topic modeling
algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this
problem very well since only very limited word co-occurrence information is
available in short texts. Therefore, short text topic modeling has already
attracted much attention from the machine learning research community in recent
years, which aims at overcoming the problem of sparseness in short texts. In
this survey, we conduct a comprehensive review of various short text topic
modeling techniques proposed in the literature. We present three categories of
methods based on Dirichlet multinomial mixture, global word co-occurrences, and
self-aggregation, with example of representative approaches in each category
and analysis of their performance on various tasks. We develop the first
comprehensive open-source library, called STTM, for use in Java that integrates
all surveyed algorithms within a unified interface, benchmark datasets, to
facilitate the expansion of new methods in this research field. Finally, we
evaluate these state-of-the-art methods on many real-world datasets and compare
their performance against one another and versus long text topic modeling
algorithm.Comment: arXiv admin note: text overlap with arXiv:1808.02215 by other author
Wavelet decomposition of software entropy reveals symptoms of malicious code
Sophisticated malware authors can sneak hidden malicious code into portable
executable files, and this code can be hard to detect, especially if encrypted
or compressed. However, when an executable file switches between code regimes
(e.g. native, encrypted, compressed, text, and padding), there are
corresponding shifts in the file's representation as an entropy signal. In this
paper, we develop a method for automatically quantifying the extent to which
patterned variations in a file's entropy signal make it "suspicious." In
Experiment 1, we use wavelet transforms to define a Suspiciously Structured
Entropic Change Score (SSECS), a scalar feature that quantifies the
suspiciousness of a file based on its distribution of entropic energy across
multiple levels of spatial resolution. Based on this single feature, it was
possible to raise predictive accuracy on a malware detection task from 50.0% to
68.7%, even though the single feature was applied to a heterogeneous corpus of
malware discovered "in the wild." In Experiment 2, we describe how
wavelet-based decompositions of software entropy can be applied to a parasitic
malware detection task involving large numbers of samples and features. By
extracting only string and entropy features (with wavelet decompositions) from
software samples, we are able to obtain almost 99% detection of parasitic
malware with fewer than 1% false positives on good files. Moreover, the
addition of wavelet-based features uniformly improved detection performance
across plausible false positive rates, both in a strings-only model (e.g., from
80.90% to 82.97%) and a strings-plus-entropy model (e.g. from 92.10% to 94.74%,
and from 98.63% to 98.90%). Overall, wavelet decomposition of software entropy
can be useful for machine learning models for detecting malware based on
extracting millions of features from executable files.Comment: Post print of paper published in Journal of Innovation in Digital
Ecosystems. This corrects typos introduced during editin
Deep Affinity Network for Multiple Object Tracking
Multiple Object Tracking (MOT) plays an important role in solving many
fundamental problems in video analysis in computer vision. Most MOT methods
employ two steps: Object Detection and Data Association. The first step detects
objects of interest in every frame of a video, and the second establishes
correspondence between the detected objects in different frames to obtain their
tracks. Object detection has made tremendous progress in the last few years due
to deep learning. However, data association for tracking still relies on hand
crafted constraints such as appearance, motion, spatial proximity, grouping
etc. to compute affinities between the objects in different frames. In this
paper, we harness the power of deep learning for data association in tracking
by jointly modelling object appearances and their affinities between different
frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN)
learns compact; yet comprehensive features of pre-detected objects at several
levels of abstraction, and performs exhaustive pairing permutations of those
features in any two frames to infer object affinities. DAN also accounts for
multiple objects appearing and disappearing between video frames. We exploit
the resulting efficient affinity computations to associate objects in the
current frame deep into the previous frames for reliable on-line tracking. Our
technique is evaluated on popular multiple object tracking challenges MOT15,
MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics
demonstrates that our approach is among the best performing techniques on the
leader board for these challenges. The open source implementation of our work
is available at https://github.com/shijieS/SST.git.Comment: To appear in IEEE TPAM
Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors
Parallel stochastic simulations tend to exploit more and more computing power
and they are now also developed for General Purpose Graphics Process Units
(GP-GPUs). Conse-quently, they need reliable random sources to feed their
applications. We propose a survey of the current Pseudo Random Numbers
Generators (PRNG) available on GPU. We give a particular focus to the recent
Mersenne Twister for Graphics Processors (MTGP) that has just been released.
Our work provides empirically checked statuses designed to initialize a
particular configuration of this generator, in order to prevent any potential
bias introduced by the parallelization of the PRNG
Data Pallets: Containerizing Storage For Reproducibility and Traceability
Trusting simulation output is crucial for Sandia's mission objectives. We
rely on these simulations to perform our high-consequence mission tasks given
national treaty obligations. Other science and modeling applications, while
they may have high-consequence results, still require the strongest levels of
trust to enable using the result as the foundation for both practical
applications and future research. To this end, the computing community has
developed workflow and provenance systems to aid in both automating simulation
and modeling execution as well as determining exactly how was some output was
created so that conclusions can be drawn from the data.
Current approaches for workflows and provenance systems are all at the user
level and have little to no system level support making them fragile, difficult
to use, and incomplete solutions. The introduction of container technology is a
first step towards encapsulating and tracking artifacts used in creating data
and resulting insights, but their current implementation is focused solely on
making it easy to deploy an application in an isolated "sandbox" and
maintaining a strictly read-only mode to avoid any potential changes to the
application. All storage activities are still using the system-level shared
storage.
This project explores extending the container concept to include storage as a
new container type we call \emph{data pallets}. Data Pallets are potentially
writeable, auto generated by the system based on IO activities, and usable as a
way to link the contained data back to the application and input deck used to
create it.Comment: 8 page
PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings
Process mining represents an important field in BPM and data mining research.
Recently, it has gained importance also for practitioners: more and more
companies are creating business process intelligence solutions. The evaluation
of process mining algorithms requires, as any other data mining task, the
availability of large amount of real-world data. Despite the increasing
availability of such datasets, they are affected by many limitations, in primis
the absence of a "gold standard" (i.e., the reference model).
This paper extends an approach, already available in the literature, for the
generation of random processes. Novelties have been introduced throughout the
work and, in particular, they involve the complete support for multiperspective
models and logs (i.e., the control-flow perspective is enriched with time and
data information) and for online settings (i.e., generation of multiperspective
event streams and concept drifts). The proposed new framework is able to almost
entirely cover the spectrum of possible scenarios that can be observed in the
real-world. The proposed approach is implemented as a publicly available Java
application, with a set of APIs for the programmatic execution of experiments.Comment: 36 pages, minor update
Energy-Performance Trade-offs in Mobile Data Transfers
By year 2020, the number of smartphone users globally will reach 3 Billion
and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic
the first time. As the number of smartphone users and the amount of data
transferred per smartphone grow exponentially, limited battery power is
becoming an increasingly critical problem for mobile devices which increasingly
depend on network I/O. Despite the growing body of research in power management
techniques for the mobile devices at the hardware layer as well as the lower
layers of the networking stack, there has been little work focusing on saving
energy at the application layer for the mobile systems during network I/O. In
this paper, to the best of our knowledge, we are first to provide an in depth
analysis of the effects of application layer data transfer protocol parameters
on the energy consumption of mobile phones. We show that significant energy
savings can be achieved with application layer solutions at the mobile systems
during data transfer with no or minimal performance penalty. In many cases,
performance increase and energy savings can be achieved simultaneously
Modeling and Evaluation of Multisource Streaming Strategies in P2P VoD Systems
In recent years, multimedia content distribution has largely been moved to the Internet, inducing broadcasters, operators and service providers to upgrade with large expenses their infrastructures. In this context, streaming solutions that rely on user devices such as set-top boxes (STBs) to offload dedicated streaming servers are particularly appropriate. In these systems, contents are usually replicated and scattered over the network established by STBs placed at users' home, and the video-on-demand (VoD) service is provisioned through streaming sessions established among neighboring STBs following a Peer-to-Peer fashion. Up to now the majority of research works have focused on the design and optimization of content replicas mechanisms to minimize server costs. The optimization of replicas mechanisms has been typically performed either considering very crude system performance indicators or analyzing asymptotic behavior. In this work, instead, we propose an analytical model that complements previous works providing fairly accurate predictions of system performance (i.e., blocking probability). Our model turns out to be a highly scalable, flexible, and extensible tool that may be helpful both for designers and developers to efficiently predict the effect of system design choices in large scale STB-VoD system
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