63,277 research outputs found
Recommended from our members
Development and Design of Energy Efficient Oil-Flooded Screw Compressors
It is estimated that about 17% of the world's generated power is used for compression. Thus all, even minor improvement of the efficiency of compressors will substantially reduce CO2 emission. This paper presents development of family of energy efficient oil-flooded screw compressors for Kirloskar Pneumatic Company Ltd. The developmental techniques adopted to improve efficiency such as introduction of superior 'N' rotor profile, rotor clearance management, performance calculation using 3D CCM (Computational Continuum Mechanics), direct parametric interface to CAD (Computer Aided Design), which contains bearing selection for complete 3D solid modelling. Also, contemporary prototyping and experimental investigation is supported by the fully computerised data acquisition and processing. The cumulative improvement of all these elements of the design process resulted in a very efficient machine which guarantees the competitive position of Kirloskar Pneumatic Company Limited in the screw compressor market
Opaque Service Virtualisation: A Practical Tool for Emulating Endpoint Systems
Large enterprise software systems make many complex interactions with other
services in their environment. Developing and testing for production-like
conditions is therefore a very challenging task. Current approaches include
emulation of dependent services using either explicit modelling or
record-and-replay approaches. Models require deep knowledge of the target
services while record-and-replay is limited in accuracy. Both face
developmental and scaling issues. We present a new technique that improves the
accuracy of record-and-replay approaches, without requiring prior knowledge of
the service protocols. The approach uses Multiple Sequence Alignment to derive
message prototypes from recorded system interactions and a scheme to match
incoming request messages against prototypes to generate response messages. We
use a modified Needleman-Wunsch algorithm for distance calculation during
message matching. Our approach has shown greater than 99% accuracy for four
evaluated enterprise system messaging protocols. The approach has been
successfully integrated into the CA Service Virtualization commercial product
to complement its existing techniques.Comment: In Proceedings of the 38th International Conference on Software
Engineering Companion (pp. 202-211). arXiv admin note: text overlap with
arXiv:1510.0142
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
In this paper, we design and evaluate a convolutional autoencoder that
perturbs an input face image to impart privacy to a subject. Specifically, the
proposed autoencoder transforms an input face image such that the transformed
image can be successfully used for face recognition but not for gender
classification. In order to train this autoencoder, we propose a novel training
scheme, referred to as semi-adversarial training in this work. The training is
facilitated by attaching a semi-adversarial module consisting of a pseudo
gender classifier and a pseudo face matcher to the autoencoder. The objective
function utilized for training this network has three terms: one to ensure that
the perturbed image is a realistic face image; another to ensure that the
gender attributes of the face are confounded; and a third to ensure that
biometric recognition performance due to the perturbed image is not impacted.
Extensive experiments confirm the efficacy of the proposed architecture in
extending gender privacy to face images
Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images
We propose a novel scheme for designing fuzzy rule based classifier. An SOFM
based method is used for generating a set of prototypes which is used to
generate a set of fuzzy rules. Each rule represents a region in the feature
space that we call the context of the rule. The rules are tuned with respect to
their context. We justified that the reasoning scheme may be different in
different context leading to context sensitive inferencing. To realize context
sensitive inferencing we used a softmin operator with a tunable parameter. The
proposed scheme is tested on several multispectral satellite image data sets
and the performance is found to be much better than the results reported in the
literature.Comment: 23 pages, 7 figure
GOGGLES: Automatic Image Labeling with Affinity Coding
Generating large labeled training data is becoming the biggest bottleneck in
building and deploying supervised machine learning models. Recently, the data
programming paradigm has been proposed to reduce the human cost in labeling
training data. However, data programming relies on designing labeling functions
which still requires significant domain expertise. Also, it is prohibitively
difficult to write labeling functions for image datasets as it is hard to
express domain knowledge using raw features for images (pixels).
We propose affinity coding, a new domain-agnostic paradigm for automated
training data labeling. The core premise of affinity coding is that the
affinity scores of instance pairs belonging to the same class on average should
be higher than those of pairs belonging to different classes, according to some
affinity functions. We build the GOGGLES system that implements affinity coding
for labeling image datasets by designing a novel set of reusable affinity
functions for images, and propose a novel hierarchical generative model for
class inference using a small development set.
We compare GOGGLES with existing data programming systems on 5 image labeling
tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a
minimum of 71% to a maximum of 98% without requiring any extensive human
annotation. In terms of end-to-end performance, GOGGLES outperforms the
state-of-the-art data programming system Snuba by 21% and a state-of-the-art
few-shot learning technique by 5%, and is only 7% away from the fully
supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management
of Dat
- …