22,974 research outputs found
The perceived quality of process discovery tools
Process discovery has seen a rise in popularity in the last decade for both
researchers and businesses. Recent developments mainly focused on the power and
the functionalities of the discovery algorithm. While continuous improvement of
these functional aspects is very important, non-functional aspects such as
visualization and usability are often overlooked. However, these aspects are
considered valuable for end-users and play an important part in the experience
of these end-users when working with a process discovery tool. A questionnaire
has been sent out to give end-users the opportunity to voice their opinion on
available process discovery tools and about the state of process discovery as a
domain in general. The results of 66 respondents are presented and compared
with the answers of 63 respondents that were contacted through one particular
software vendor's employee and customer base (i.e., Celonis)
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems
recently achieve promising progress to take over tedious examinations by
cardiologists. However, their vulnerability to adversarial attacks still lack
comprehensive investigation. The existing attacks in image domain could not be
directly applicable due to the distinct properties of ECGs in visualization and
dynamic properties. Thus, this paper takes a step to thoroughly explore
adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the
properties of ECGs to design effective attacks schemes under two attacks models
respectively. Our results demonstrate the blind spots of DNN-powered diagnosis
systems under adversarial attacks, which calls attention to adequate
countermeasures.Comment: Accepted by AAAI 202
Applied business analytics approach to IT projects – Methodological framework
The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been
highly successful in solving complex vision problems. However, these deep
models are perceived as "black box" methods considering the lack of
understanding of their internal functioning. There has been a significant
recent interest in developing explainable deep learning models, and this paper
is an effort in this direction. Building on a recently proposed method called
Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide
better visual explanations of CNN model predictions, in terms of better object
localization as well as explaining occurrences of multiple object instances in
a single image, when compared to state-of-the-art. We provide a mathematical
derivation for the proposed method, which uses a weighted combination of the
positive partial derivatives of the last convolutional layer feature maps with
respect to a specific class score as weights to generate a visual explanation
for the corresponding class label. Our extensive experiments and evaluations,
both subjective and objective, on standard datasets showed that Grad-CAM++
provides promising human-interpretable visual explanations for a given CNN
architecture across multiple tasks including classification, image caption
generation and 3D action recognition; as well as in new settings such as
knowledge distillation.Comment: 17 Pages, 15 Figures, 11 Tables. Accepted in the proceedings of IEEE
Winter Conf. on Applications of Computer Vision (WACV2018). Extended version
is under review at IEEE Transactions on Pattern Analysis and Machine
Intelligenc
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