52,780 research outputs found
Cyber security investigation for Raspberry Pi devices
Big Data on Cloud application is growing rapidly. When the cloud is attacked, the investigation relies on digital forensics evidence. This paper proposed the data collection via Raspberry Pi devices, in a healthcare situation. The significance of this work is that could be expanded into a digital device array that takes big data security issues into account. There are many potential impacts in health area. The field of Digital Forensics Science has been tagged as a reactive science by some who believe research and study in the field often arise as a result of the need to respond to event which brought about the needs for investigation; this work was carried as a proactive research that will add knowledge to the field of Digital Forensic Science.
The Raspberry Pi is a cost-effective, pocket sized computer that has gained global recognition since its development in 2008; with the wide spread usage of the device for different computing purposes. Raspberry Pi can potentially be a cyber security device, which can relate with forensics investigation in the near future. This work has used a systematic approach to study the structure and operation of the device and has established security issues that the widespread usage of the device can pose, such as health or smart city. Furthermore, its evidential information applied in security will be useful in the event that the device becomes a subject of digital forensic investigation in the foreseeable future. In healthcare system, PII (personal identifiable information) is a very important issue. When Raspberry Pi plays a processor role, its security is vital; consequently, digital forensics investigation on the Raspberry Pies becomes necessary
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI), 201
On the efficiency of reductions in µ-SIMD media extensions
Many important multimedia applications contain a significant fraction of reduction operations. Although, in general, multimedia applications are characterized for having high amounts of Data Level Parallelism, reductions and accumulations are difficult to parallelize and show a poor tolerance to increases in the latency of the instructions. This is specially significant for µ-SIMD extensions such as MMX or AltiVec. To overcome the problem of reductions in µ-SIMD ISAs, designers tend to include more and more complex instructions able to deal with the most common forms of reductions in multimedia. As long as the number of processor pipeline stages grows, the number of cycles needed to execute these multimedia instructions increases with every processor generation, severely compromising performance. The paper presents an in-depth discussion of how reductions/accumulations are performed in current µ-SIMD architectures and evaluates the performance trade-offs for near-future highly aggressive superscalar processors with three different styles of µ-SIMD extensions. We compare a MMX-like alternative to a MDMX-like extension that has packed accumulators to attack the reduction problem, and we also compare it to MOM, a matrix register ISA. We show that while packed accumulators present several advantages, they introduce artificial recurrences that severely degrade performance for processors with high number of registers and long latency operations. On the other hand, the paper demonstrates that longer SIMD media extensions such as MOM can take great advantage of accumulators by exploiting the associative parallelism implicit in reductions.Peer ReviewedPostprint (published version
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as established algorithms designed for this purpose. In decoding
EEG pathology, both ConvNets reached substantially better accuracies (about 6%
better, ~85% vs. ~79%) than the only published result for this dataset, and
were still better when using only 1 minute of each recording for training and
only six seconds of each recording for testing. We used automated methods to
optimize architectural hyperparameters and found intriguingly different ConvNet
architectures, e.g., with max pooling as the only nonlinearity. Visualizations
of the ConvNet decoding behavior showed that they used spectral power changes
in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
other features, consistent with expectations derived from spectral analysis of
the EEG data and from the textual medical reports. Analysis of the textual
medical reports also highlighted the potential for accuracy increases by
integrating contextual information, such as the age of subjects. In summary,
the ConvNets and visualization techniques used in this study constitute a next
step towards clinically useful automated EEG diagnosis and establish a new
baseline for future work on this topic.Comment: Published at IEEE SPMB 2017 https://www.ieeespmb.org/2017
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