Research on Information Resilience

Paper 1: "Information Resilience: The Nexus of Responsible and Agile Approaches to Information Use"

Authors: Sadiq, S., Aryani, A., Demartini, G., Hua, W., Indulska, M., Burton-Jones, A., Khosravi, H., et al.

Published in: The VLDB Journal

Publication Date: 2022

This paper explores the concept of information resilience and its relationship with responsible and agile approaches to information use. The authors argue that information resilience is crucial in today's rapidly changing and uncertain environment. They propose a framework that integrates responsible and agile approaches to enhance information resilience. The framework emphasizes the importance of ethical considerations, data governance, and adaptability in managing and using information effectively.

Paper 2: "Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model Approach"

Authors: Samtani, S., Chai, Y., and Chen, H.

Published in: MIS Quarterly

Publication Date: In press

This upcoming paper focuses on proactive cyber threat intelligence and proposes an attention-based deep structured semantic model approach to link exploits from the dark web to known vulnerabilities. The authors highlight the importance of proactive measures in identifying and mitigating cyber threats. Their approach leverages deep learning techniques to analyze and understand the semantic relationships between exploits and vulnerabilities, enabling organizations to take proactive actions to protect their systems and data.

Paper 3: "The Halo Effect in Multicomponent Ratings and its Implications for Recommender Systems: The Case Of Yahoo! Movies"

Authors: Sahoo, N., Krishnan, R., Duncan, G., and Callan, J.

Published in: Information Systems Research

Publication Date: 2012

This research note investigates the halo effect in multicomponent ratings and its implications for recommender systems, using Yahoo! Movies as a case study. The authors highlight the tendency of users to rate all components of a product or service similarly, leading to biased recommendations. They propose a method to mitigate the halo effect and improve the accuracy of recommender systems. The findings have implications for designing more effective recommendation algorithms and improving user satisfaction.

Paper 4: "Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach"

Authors: Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., and Lee, K. C.

Published in: MIS Quarterly

Publication Date: 2020

This paper explores the use of visual data analytics and deep learning techniques to enhance social media analysis. The authors argue that visual data can provide valuable insights and complement textual analysis in understanding social media content. They propose a deep learning approach that combines visual and textual features to improve the accuracy and effectiveness of social media analysis. The findings highlight the potential of visual data analytics in extracting meaningful information from social media platforms.

Overall, these research papers contribute to the understanding of information resilience, responsible information use, proactive cyber threat intelligence, and the use of visual data analytics in social media analysis. They provide valuable insights and propose innovative approaches to address the challenges and opportunities in these areas.

Summary of Scientific Papers

1. "Multitask Learning" by Caruana, R. (1997)

This paper discusses the concept of multitask learning and its applications in machine learning. It explores the idea of leveraging knowledge from multiple related tasks to improve the performance of individual tasks. The paper provides an overview of different approaches to multitask learning and presents experimental results to demonstrate its effectiveness.

2. "Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges" by Caruana, R., Lundberg, S., Ribeiro, M. T., Nori, H., and Jenkins, S. (2020)

In this paper, the authors discuss the importance of intelligible and explainable machine learning models. They highlight the challenges associated with building models that are not only accurate but also interpretable. The paper presents best practices for designing and evaluating interpretable machine learning models and provides insights into practical challenges in this field.

3. "Efficient Influence Maximization in Social Networks" by Chen, W., Wang, Y., and Yang, S. (2009)

This paper focuses on the problem of influence maximization in social networks. It addresses the challenge of identifying a small set of influential individuals in a network to maximize the spread of information or influence. The paper proposes an efficient algorithm for influence maximization and presents experimental results to demonstrate its effectiveness.

4. "Double/Debiased Machine Learning for Treatment and Structural Parameters" by Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J. (2018)

This paper introduces the concept of double/debiased machine learning for estimating treatment and structural parameters. It discusses the challenges associated with estimating causal effects in observational studies and presents a framework that combines machine learning techniques with econometric methods to address these challenges. The paper provides theoretical results and empirical examples to illustrate the effectiveness of double/debiased machine learning.

5. "Active Learning with Statistical Models" by Cohn, D. A., Ghahramani, Z., and Jordan, M. I. (1996)

This paper explores the concept of active learning with statistical models. It discusses the idea of selecting informative samples to label in order to improve the performance of a learning algorithm. The paper presents different active learning strategies and provides experimental results to demonstrate their effectiveness in various domains.

Please note that the publication dates for the papers are as follows:

  1. "Multitask Learning" - 1997
  2. "Intelligible and Explainable Machine Learning" - 2020
  3. "Efficient Influence Maximization in Social Networks" - 2009
  4. "Double/Debiased Machine Learning for Treatment and Structural Parameters" - 2018
  5. "Active Learning with Statistical Models" - 1996

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