scholarly journals A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

2018 ◽  
Vol 9 (1) ◽  
pp. 1-49 ◽  
Author(s):  
Iulian Vlad Serban ◽  
Ryan Lowe ◽  
Peter Henderson ◽  
Laurent Charlin ◽  
Joelle Pineau
Keyword(s):  
Transfer Learning ◽  
Expert Knowledge ◽  
Learning Objective ◽  
Data Driven ◽  
Evaluation Metrics ◽  
Dialogue Systems ◽  
Speech And Language ◽  
Language Understanding ◽  
The Past ◽  
Use Of Data

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

scholarly journals CAiRE: An End-to-End Empathetic Chatbot

2020 ◽  
Vol 34 (09) ◽  
pp. 13622-13623
Author(s):  
Zhaojiang Lin ◽  
Peng Xu ◽  
Genta Indra Winata ◽  
Farhad Bin Siddique ◽  
Zihan Liu ◽  
...  
Keyword(s):  
User Interface ◽  
Active Learning ◽  
Transfer Learning ◽  
User Feedback ◽  
Data Driven ◽  
Dialogue Systems ◽  
Generation Task ◽  
Web Based ◽  
End To End ◽  
Negative Training

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

The transdiagnostic revolution in neurodevelopmental disorders

2021 ◽  
Author(s):  
Duncan Astle ◽  
Joni Holmes ◽  
Rogier Kievit ◽  
Susan Elizabeth Gathercole
Keyword(s):  
Diagnostic Criteria ◽  
Study Design ◽  
Neurodevelopmental Disorders ◽  
Data Driven ◽  
Children And Families ◽  
The Past ◽  
Use Of Data ◽  
Approaches To Study ◽  
Underlying Reality ◽  
Guide Intervention

Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for the research of those disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms, and finding the best route to supporting them.

scholarly journals Digitalization of security in the energy sector: evolution of EU law and policy

2020 ◽  
Vol 13 (4) ◽  
pp. 353-367
Author(s):  
Mikko Rajavuori ◽  
Kaisa Huhta
Keyword(s):  
Policy Instruments ◽  
Energy Transition ◽  
Energy Sector ◽  
Data Driven ◽  
Eu Law ◽  
The European Union ◽  
The Past ◽  
Use Of Data ◽  
Law And Policy ◽  
Security Discourse

Abstract This article analyses the dynamics and implications of the digitalization of security in the energy sector. Based on an evolutionary review of legal and policy instruments, we map the pace and internal dynamics of the digitalization of security in the European Union over the past 15 years. Our analysis reveals substantial changes in the conceptions and dynamics of security in the energy sector. First, we find that digitalization has only recently penetrated into the core of the energy sector’s security paradigm. Secondly, we uncover a significant disconnect in the conceptualization of the risk as against the opportunities associated with digitalization. Thirdly, we identify the growing influence of cross-sectoral instruments in the energy sector. Fourthly, we find that energy security does not feature in the overarching security discourse relating to the use of data-driven technologies in the energy sector. The findings illustrate the difficulties that managers, policymakers and researchers face when trying to keep up with the rapid technological change in the energy transition and the ensuing evolution of the energy sector’s security paradigm.

scholarly journals A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

2021 ◽  
Vol 1 ◽  
Author(s):  
Constantin Falk ◽  
Ron Van de Sand ◽  
Sandra Corasaniti ◽  
Jörg Reiff-Stephan
Keyword(s):  
Operating Conditions ◽  
Data Driven ◽  
Sensor Data ◽  
Comparison Study ◽  
True Negative ◽  
The Past ◽  
Use Of Data ◽  
Data Driven Approach ◽  
System States ◽  
System Operating

Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Fingerprint Presentation Attack Detection Using Transfer Learning Approach

2021 ◽  
Vol 17 (1) ◽  
pp. 53-67
Author(s):  
Rajneesh Rani ◽  
Harpreet Singh
Keyword(s):  
Transfer Learning ◽  
Attack Detection ◽  
Classification Error ◽  
Learning Approach ◽  
Biometric Authentication ◽  
The Past ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Software And Hardware ◽  
Presentation Attack Detection

In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

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