A Novel Approach of Prioritizing Use Case Scenarios

2007 ◽  
Author(s):  
Debasish Kundu ◽  
Debasis Samanta
Keyword(s):  
Use Case ◽  
Novel Approach

A Novel Approach to Construct Semantic Mashup using Patterns

2017 ◽  
Vol 12 (1) ◽  
pp. 19-36
Author(s):  
Khayra Bencherif ◽  
Djamel Amar Bensaber ◽  
Mimoun Malki
Keyword(s):  
User Interface ◽  
Data Integration ◽  
Web 2.0 ◽  
Travel Agency ◽  
Use Case ◽  
Web Resources ◽  
Single Interface ◽  
Automatic Integration ◽  
Novel Approach ◽  
Automatic Matching

With the coming of Web 2.0, several technologies are developed to facilitate creating, sharing and reusing of web resources. In this context, the mashup is a novel approach that allows the user to aggregate multiples services to create a single one with a new user interface. However, a key limitation of existing mashups applications is the need to compute semantic and syntactic similarities between data in different services and create or modify workflows in applications mashups without enlisting the talents of the original developers or vendor. In fact, automatic matching tools help users to facilitate automatic integration of both data and APIs without knowing their structure and semantics. In this paper, the authors suggest a novel approach which consists in building a semantic mashup using a matching tool, domain ontology and a set of patterns to facilitate and automate services and data integration. As a study use case, they develop a semantic mashup application for a travel agency that provides a single interface to users.

scholarly journals Recognition of Typical Locomotion Activities Based on the Sensor Data of a Smartphone in Pocket or Hand

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6559
Author(s):  
Markus Ebner ◽  
Toni Fetzer ◽  
Markus Bullmann ◽  
Frank Deinzer ◽  
Marcin Grzegorzek
Keyword(s):  
Activity Recognition ◽  
Indoor Localization ◽  
Research Area ◽  
The Other ◽  
Sensor Data ◽  
Use Case ◽  
Sources Of Information ◽  
Novel Approach ◽  
Current Location ◽  
Radio Signals

With the ubiquity of smartphones, the interest in indoor localization as a research area grew. Methods based on radio data are predominant, but due to the susceptibility of these radio signals to a number of dynamic influences, good localization solutions usually rely on additional sources of information, which provide relative information about the current location. Part of this role is often taken by the field of activity recognition, e.g., by estimating whether a pedestrian is currently taking the stairs. This work presents different approaches for activity recognition, considering the four most basic locomotion activities used when moving around inside buildings: standing, walking, ascending stairs, and descending stairs, as well as an additional messing around class for rejections. As main contribution, we introduce a novel approach based on analytical transformations combined with artificially constructed sensor channels, and compare that to two approaches adapted from existing literature, one based on codebooks, the other using statistical features. Data is acquired using accelerometer and gyroscope only. In addition to the most widely adopted use-case of carrying the smartphone in the trouser pockets, we will equally consider the novel use-case of hand-carried smartphones. This is required as in an indoor localization scenario, the smartphone is often used to display a user interface of some navigation application and thus needs to be carried in hand. For evaluation the well known MobiAct dataset for the pocket-case as well as a novel dataset for the hand-case were used. The approach based on analytical transformations surpassed the other approaches resulting in accuracies of 98.0% for pocket-case and 81.8% for the hand-case trained on the combination of both datasets. With activity recognition in the supporting role of indoor localization, this accuracy is acceptable, but has room for further improvement.

scholarly journals A roadmap to neural automatic post-editing: an empirical approach

2020 ◽  
Vol 34 (2-3) ◽  
pp. 67-96
Author(s):  
Dimitar Shterionov ◽  
Félix do Carmo ◽  
Joss Moorkens ◽  
Murhaf Hossari ◽  
Joachim Wagner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Quality Standards ◽  
Use Case ◽  
Collaborative Project ◽  
Translation Quality ◽  
Decision Points ◽  
Novel Approach ◽  
Statistical Mt ◽  
Required Quality

Abstract In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficult to select the most suitable approach for a given use case. In this article, we systematically analyse these different parameters with respect to NPE performance. We build an NPE “roadmap” to trace the different decision points and train a set of systems selecting different options through the roadmap. We also propose a novel approach for APE with data augmentation. We then analyse the performance of 15 of these systems and identify the best ones. In fact, the best systems are the ones that follow the newly-proposed method. The work presented in this article follows from a collaborative project between Microsoft and the ADAPT centre. The data provided by Microsoft originates from phrase-based statistical MT (PBSMT) systems employed in production. All tested NPE systems significantly increase the translation quality, proving the effectiveness of neural post-editing in the context of a commercial translation workflow that leverages PBSMT.

scholarly journals Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

2021 ◽  
Author(s):  
Pieter Van Molle ◽  
Tim Verbelen ◽  
Bert Vankeirsbilck ◽  
Jonas De Vylder ◽  
Bart Diricx ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Use Case ◽  
Bhattacharyya Coefficient ◽  
Output Uncertainty ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Network Approaches

AbstractModern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique estimates the moments of the output distribution through sampling with different dropout masks. The output uncertainty of a neural network is then approximated as the sample variance. In this paper, we highlight the limitations of such a variance-based uncertainty metric and propose an novel approach. Our approach is based on the overlap between output distributions of different classes. We show that our technique leads to a better approximation of the inter-class output confusion. We illustrate the advantages of our method using benchmark datasets. In addition, we apply our metric to skin lesion classification—a real-world use case—and show that this yields promising results.

Manual guidance for industrial robot programming

2015 ◽  
Vol 42 (5) ◽  
pp. 457-465 ◽  
Author(s):  
Daniele Massa ◽  
Massimo Callegari ◽  
Cristina Cristalli
Keyword(s):  
Industrial Robot ◽  
Robot Programming ◽  
Use Case ◽  
Programming By Demonstration ◽  
Content Type ◽  
Safety Standards ◽  
Novel Approach ◽  
Complete State ◽  
Speed Up ◽  
Practical Implications

Purpose – This paper aims to deal with the problem of programming robots in industrial contexts, where the need of easy programming is increasing, while robustness and safety remain fundamental aspects. Design/methodology/approach – A novel approach of robot programming can be identified with the manual guidance that permits to the operator to freely move the robot through its task; the task can then be taught using Programming by Demonstration methods or simple reproduction. Findings – In this work, the different ways to achieve manual guidance are discussed and an implementation using a force/torque sensor is provided. Experimental results and a use case are also presented. Practical implications – The use case shows how this methodology can be used with an industrial robot. An implementation in industrial contexts should be adjusted accordingly to ISO safety standards as described in the paper. Originality/value – This paper presents a complete state-of-the-art of the problem and shows a real practical use case where the approach presented could be used to speed up the teaching process.

SoundSight: A Mobile Sensory Substitution Device that Sonifies Colour, Distance, and Temperature

2019 ◽  
Author(s):  
Giles Hamilton-Fletcher ◽  
James Alvarez ◽  
Marianna Obrist ◽  
Jamie Ward
Keyword(s):  
Theoretical Background ◽  
Sensory Substitution ◽  
Use Case ◽  
Novel Approach ◽  
Smartphone Technology ◽  
Degree Of Control ◽  
Common Problems ◽  
Colour Distance ◽  
Selection Of

Depth, colour, and thermal images contain practical and actionable information for the visually-impaired. Conveying this information through alternative modalities such as audition creates new interaction possibilities for users as well as opportunities to study neuroplasticity. The ‘SoundSight’ App (www.SoundSight.co.uk) provides a smartphone platform that allows 3D position, colour, and thermal information to directly control thousands of high-quality sounds in real-time to create completely unique and responsive soundscapes for the user. These sounds could be anything - tones, rainfall, speech, instruments, or even full musical tracks. Users have a fine degree of control over how these sounds are presented through controlling the timing and selection of sounds played at a given moment. Through utilising smartphone technology with a novel approach to sonification, the SoundSight App provides a cheap, widely-accessible, scalable, and flexible sensory tool. In this paper we discuss common problems encountered with assistive sensory tools reaching long-term adoption, how our device seeks to address these problems, its theoretical background, its technical implementation, and finally we showcase a range of use case scenarios for scientists, artists, and the blind community.

scholarly journals Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7572
Author(s):  
Sorin Liviu Jurj ◽  
Dominik Grundt ◽  
Tino Werner ◽  
Philipp Borchers ◽  
Karina Rothemann ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Use Case ◽  
Cruise Control ◽  
Fleet Size ◽  
Physical Knowledge ◽  
Novel Approach ◽  
Longitudinal Distance ◽  
The Ideal

This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).

scholarly journals Estimating point-of-interest rating based on visitors geospatial behaviour

2019 ◽  
Vol 16 (1) ◽  
pp. 131-154 ◽  
Author(s):  
Matej Senozetnik ◽  
Luka Bradesko ◽  
Tine Subic ◽  
Zala Herga ◽  
Jasna Urbancic ◽  
...  
Keyword(s):  
Support Vector ◽  
Use Case ◽  
Machine Learning Methods ◽  
Applied Machine Learning ◽  
Point Of Interest ◽  
Points Of Interest ◽  
Novel Approach ◽  
Gps Traces ◽  
Crucial Part ◽  
Insufficient Number

Rating of different services, products and experiences plays an important role in our digitally assisted day-to-day life. It helps us make decisions when we are indecisive, uninformed or inexperienced. Traditionally, ratings depend on the willingness of existing customers to provide them. This often leads to biased (due to the insufficient number of votes) or nonexistent ratings. This was the motivation for our research, which aims to provide automatic star rating. The paper presents an approach to extracting points-of-interest from various sources and a novel approach to estimating point-of-interest ratings, based on geospatial data of their visitors. Our research is applied to campsite dataset where the community is still developing and more than thirty percent of camps are unrated. Our study use case addresses a realword problem of motorhome users visiting campsites in European countries. The dataset includes GPS traces from 10 motorhomes that were collected over a period of 2 years. To estimate star ratings of points-of-interest we applied machine learning methods including support vector machine, linear regression, random forest and decision trees. Our experimental results show that the duration of visit, which is a crucial part of the proposed approach, is an indicative feature for predicting camp ratings.

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