State-of-the-Art in Automated Story Generation Systems Research

2021 ◽  
pp. 1-55
Author(s):  
Rebeca Amaya Ansag ◽  
Avelino J. Gonzalez
Keyword(s):  
State Of The Art ◽  
Story Generation ◽  
Systems Research

scholarly journals A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation

2020 ◽  
Vol 8 ◽  
pp. 93-108 ◽  
Author(s):  
Jian Guan ◽  
Fei Huang ◽  
Zhihao Zhao ◽  
Xiaoyan Zhu ◽  
Minlie Huang
Keyword(s):  
Long Range ◽  
Temporal Order ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Fine Tuning ◽  
Causal Relationships ◽  
Commonsense Knowledge ◽  
Language Generation ◽  
Story Generation ◽  
Global Coherence

Story generation, namely, generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of long-range coherence in generated stories. We conjecture that this is because of the difficulty of associating relevant commonsense knowledge, understanding the causal relationships, and planning entities and events with proper temporal order. In this paper, we devise a knowledge-enhanced pretraining model for commonsense story generation. We propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories. To further capture the causal and temporal dependencies between the sentences in a reasonable story, we use multi-task learning, which combines a discriminative objective to distinguish true and fake stories during fine-tuning. Automatic and manual evaluation shows that our model can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence.

scholarly journals Evaluating recommender systems for AI-driven biomedical informatics

2020 ◽  
Author(s):  
William La Cava ◽  
Heather Williams ◽  
Weixuan Fu ◽  
Steve Vitale ◽  
Durga Srivatsan ◽  
...  
Keyword(s):  
Septic Shock ◽  
Recommender Systems ◽  
Data Science ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Optimal Algorithm ◽  
Supplementary Information ◽  
Biomedical Data ◽  
Classification Problems ◽  
Systems Research

Abstract Motivation Many researchers with domain expertise are unable to easily apply machine learning (ML) to their bioinformatics data due to a lack of ML and/or coding expertise. Methods that have been proposed thus far to automate ML mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly. Here, we study a method of automating biomedical data science using a web-based AI platform to recommend model choices and conduct experiments. We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user’s experiments as well as prior knowledge. To validate this framework, we conduct an experiment on 165 classification problems, comparing to state-of-the-art, automated approaches. Finally, we use this tool to develop predictive models of septic shock in critical care patients. Results We find that matrix factorization-based recommendation systems outperform metalearning methods for automating ML. This result mirrors the results of earlier recommender systems research in other domains. The proposed AI is competitive with state-of-the-art automated ML methods in terms of choosing optimal algorithm configurations for datasets. In our application to prediction of septic shock, the AI-driven analysis produces a competent ML model (AUROC 0.85±0.02) that performs on par with state-of-the-art deep learning results for this task, with much less computational effort. Availability and implementation PennAI is available free of charge and open-source. It is distributed under the GNU public license (GPL) version 3. Supplementary information Supplementary data are available at Bioinformatics online.

Information security culture – state-of-the-art review between 2000 and 2013

2015 ◽  
Vol 23 (3) ◽  
pp. 246-285 ◽  
Author(s):  
Fredrik Karlsson ◽  
Joachim Åström ◽  
Martin Karlsson
Keyword(s):  
Information Security ◽  
Research Methods ◽  
State Of The Art ◽  
Assessment Tools ◽  
Future Research ◽  
Research Topics ◽  
Content Type ◽  
Security Culture ◽  
Systems Research ◽  
Information Security Culture

Purpose – The aim of this paper is to survey existing information security culture research to scrutinise the kind of knowledge that has been developed and the way in which this knowledge has been brought about. Design/methodology/approach – Results are based on a literature review of information security culture research published between 2000 and 2013 (December). Findings – This paper can conclude that existing research has focused on a broad set of research topics, but with limited depth. It is striking that the effects of different information security cultures have not been part of that focus. Moreover, existing research has used a small repertoire of research methods, a repertoire that is more limited than in information systems research in general. Furthermore, an extensive part of the research is descriptive, philosophical or theoretical – lacking a structured use of empirical data – which means that it is quite immature. Research limitations/implications – Findings call for future research that: addresses the effects of different information security cultures; addresses the identified research topics with greater depth; focuses more on generating theories or testing theories to increase the maturity of this subfield of information security research; and uses a broader set of research methods. It would be particularly interesting to see future studies that use intervening or ethnographic approaches because, to date, these have been completely lacking in existing research. Practical implications – Findings show that existing research is, to a large extent, descriptive, philosophical or theoretical. Hence, it is difficult for practitioners to adopt these research results, such as frameworks for cultivating or assessment tools, which have not been empirically validated. Originality/value – Few state-of-the-art reviews have sought to assess the maturity of existing research on information security culture. Findings on types of research methods used in information security culture research extend beyond the existing knowledge base, which allows for a critical discussion about existing research in this sub-discipline of information security.

Validation in Information Systems Research: A State-of-the-Art Assessment

MIS Quarterly ◽  
2001 ◽  
Vol 25 (1) ◽  
pp. 1 ◽  
Author(s):  
Marie-Claude Boudreau ◽  
David Gefen ◽  
Detmar W. Straub
Keyword(s):  
Information Systems ◽  
State Of The Art ◽  
Systems Research ◽  
Information Systems Research ◽  
Art Assessment

scholarly journals Infrastructure BIM Platform for Lifecycle Management

2021 ◽  
Vol 11 (21) ◽  
pp. 10310
Author(s):  
Keunyoung Jang ◽  
Jong-Woo Kim ◽  
Ki-Beom Ju ◽  
Yun-Kyu An
Keyword(s):  
Construction Management ◽  
State Of The Art ◽  
Time History ◽  
Learning Networks ◽  
Planning And Design ◽  
Systems Research ◽  
History Analysis ◽  
Processing Techniques ◽  
Lifecycle Management ◽  
Art Techniques

Recently, the application of the BIM technique to infrastructure lifecycle management has increased rapidly to improve the efficiency of infrastructure management systems. Research on the lifecycle management of infrastructure, from planning and design to construction and management, has been carried out. Therefore, a systematic review of the literature on recent research is performed to analyze the current state of the BIM technique. State-of-the-art techniques for infrastructure lifecycle management, such as unmanned robots, sensors and processing techniques, artificial intelligence, etc., are also reviewed. An infrastructure BIM platform framework composed of BIM and state-of-the-art techniques is then proposed. The proposed platform is a web-based platform that contains quantity, schedule (4D), and cost (5D) construction management, and the monitoring systems enable collaboration with stakeholders in a Common Data Environment (CDE). The lifecycle management methodology, after infrastructure construction, is then completed and is developed using state-of-the-art techniques using unmanned robots, scan-to-BIM, and deep learning networks, etc. It is confirmed that collaboration with stakeholders in the CDE in construction management is possible using an infrastructure BIM platform. Moreover, lifecycle management of infrastructure is possible by systematic management, such as time history analysis, damage growth prediction, decision of repair and demolition, etc., using a regular inspection database based on an infrastructure BIM platform.

Virtual Team Research

2007 ◽  
Vol 38 (1) ◽  
pp. 12-59 ◽  
Author(s):  
Shu Z. Schiller ◽  
Munir Mandviwalla
Keyword(s):  
Virtual Teams ◽  
State Of The Art ◽  
Virtual Team ◽  
Theoretical Development ◽  
Frequency Pattern ◽  
Team Research ◽  
Systems Research ◽  
Information Systems Research ◽  
Depth Analysis ◽  
Theory Application

Recent information systems research has studied various aspects of virtual teams. However, the foundations and theoretical development of virtual team research remain unclear. We propose that an important way to move forward is to accelerate the process of theorizing and theory appropriation. This article presents an in-depth analysis of the current state of the art of theory application and development in virtual team research. We identify the frequency, pattern of use, and ontological basis of 25 virtual team-relevant theories. A researcher’s tool kit is presented to promote future theory application and appropriation. The tool kit consists of a descriptive and analytical database of theories relevant for virtual team research. We also present a framework for appropriating virtual team theories based on seven criteria. A detailed example demonstrates the application of the theory appropriation framework. The article contributes to the literature by presenting the state of the art of theory use in virtual team research and by providing a framework for appropriating reference-discipline theories.

scholarly journals Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

2020 ◽  
Vol 10 (21) ◽  
pp. 7748
Author(s):  
Zeshan Fayyaz ◽  
Mahsa Ebrahimian ◽  
Dina Nawara ◽  
Ahmed Ibrahim ◽  
Rasha Kashef
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Qualitative Evaluation ◽  
Potential Influence ◽  
Systems Research ◽  
Current State

Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

The State-of-the-Art in Nano-Turning

2007 ◽  
Author(s):  
Michael C. Schmittdiel ◽  
Rodolfo E. Haber Guerra ◽  
A´ngel Escribano ◽  
Javier Escribano
Keyword(s):  
State Of The Art ◽  
Single Point ◽  
The State ◽  
Tool Geometry ◽  
Machining Parameters ◽  
Ongoing Research ◽  
Nano Scale ◽  
Systems Research ◽  
Automation Systems ◽  
The Impact

The state-of-the-art in nano-turning is evolving rapidly due to the economic and application benefits that nano-scale engineering creates. This paper describes the state of the art in nano-turning with a focus on single point diamond tools (SPDT) and integrated sensory systems. Research in this area has thus far lagged behind other nano-scale research efforts in the chemical and biological sciences. Discussion of the fundamentals of nano-scale turning is presented along with the state-of-the-art in sensor systems. Surface finish is a critical performance characteristic for nano-turned parts, which is detailed in terms of the impact of machining parameters, tool geometry, and material anisotropy. Ongoing research into computer modeling of the cutting mechanics at the nano-scale is presented. Sensors and automation systems for nano-turning are discussed with a focus on active control and in-process adjustments. Finally, recommendations are given for the future of nano-turning and nano-scale machining in general.

scholarly journals Expert Finding Systems: A Systematic Review

2019 ◽  
Vol 9 (20) ◽  
pp. 4250 ◽  
Author(s):  
Omayma Husain ◽  
Naomie Salim ◽  
Rose Alinda Alias ◽  
Samah Abdelsalam ◽  
Alzubair Hassan
Keyword(s):  
Systematic Review ◽  
Conference Proceedings ◽  
Information Needs ◽  
State Of The Art ◽  
Future Research ◽  
Expert Finding ◽  
Specific Nature ◽  
Systems Research ◽  
Data Overload ◽  
Expertise Retrieval

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

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