116,636 research outputs found
Intelligent assistance for data pre-processing
A data mining algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. Typically, a dataset needs to be pre-processed before being mined. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives. As a consequence, non-experienced users become overwhelmed with pre-processing alternatives. In this paper, we show that the problem can be addressed by automating the pre-processing with the support of meta-learning. To this end, we analyzed a wide range of data pre-processing techniques and a set of classification algorithms. For each classification algorithm that we consider and a given dataset, we are able to automatically suggest the transformations that improve the quality of the results of the algorithm on the dataset. Our approach will help non-expert users to more effectively identify the transformations appropriate to their applications, and hence to achieve improved results.Postprint (author's final draft
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting
Modern urbanization is demanding smarter technologies to improve a variety of
applications in intelligent transportation systems to relieve the increasing
amount of vehicular traffic congestion and incidents. Existing incident
detection techniques are limited to the use of sensors in the transportation
network and hang on human-inputs. Despite of its data abundance, social media
is not well-exploited in such context. In this paper, we develop an automated
traffic alert system based on Natural Language Processing (NLP) that filters
this flood of information and extract important traffic-related bullets. To
this end, we employ the fine-tuning Bidirectional Encoder Representations from
Transformers (BERT) language embedding model to filter the related traffic
information from social media. Then, we apply a question-answering model to
extract necessary information characterizing the report event such as its exact
location, occurrence time, and nature of the events. We demonstrate the adopted
NLP approaches outperform other existing approach and, after effectively
training them, we focus on real-world situation and show how the developed
approach can, in real-time, extract traffic-related information and
automatically convert them into alerts for navigation assistance applications
such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering
Management Society International Conference (TEMSCON'20), Metro Detroit,
Michigan (USA
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity
PRESISTANT: Learning based assistant for data pre-processing
Data pre-processing is one of the most time consuming and relevant steps in a
data analysis process (e.g., classification task). A given data pre-processing
operator (e.g., transformation) can have positive, negative or zero impact on
the final result of the analysis. Expert users have the required knowledge to
find the right pre-processing operators. However, when it comes to non-experts,
they are overwhelmed by the amount of pre-processing operators and it is
challenging for them to find operators that would positively impact their
analysis (e.g., increase the predictive accuracy of a classifier). Existing
solutions either assume that users have expert knowledge, or they recommend
pre-processing operators that are only "syntactically" applicable to a dataset,
without taking into account their impact on the final analysis. In this work,
we aim at providing assistance to non-expert users by recommending data
pre-processing operators that are ranked according to their impact on the final
analysis. We developed a tool PRESISTANT, that uses Random Forests to learn the
impact of pre-processing operators on the performance (e.g., predictive
accuracy) of 5 different classification algorithms, such as J48, Naive Bayes,
PART, Logistic Regression, and Nearest Neighbor. Extensive evaluations on the
recommendations provided by our tool, show that PRESISTANT can effectively help
non-experts in order to achieve improved results in their analytical tasks
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity
This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context
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