scholarly journals A Systematic Review on Model Watermarking for Neural Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Franziska Boenisch
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Neural Networks ◽  
Intellectual Property ◽  
Digital Watermarking ◽  
Security Requirements ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Computational Resources

Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered the intellectual property of the legitimate parties who have trained them, which makes their protection against stealing, illegitimate redistribution, and unauthorized application an urgent need. Digital watermarking presents a strong mechanism for marking model ownership and, thereby, offers protection against those threats. This work presents a taxonomy identifying and analyzing different classes of watermarking schemes for ML models. It introduces a unified threat model to allow structured reasoning on and comparison of the effectiveness of watermarking methods in different scenarios. Furthermore, it systematizes desired security requirements and attacks against ML model watermarking. Based on that framework, representative literature from the field is surveyed to illustrate the taxonomy. Finally, shortcomings and general limitations of existing approaches are discussed, and an outlook on future research directions is given.

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

scholarly journals 3D Printing in Construction: Current Status, Implementation Hindrances, and Development Agenda

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Ning ◽  
Tong Liu ◽  
Chunlin Wu ◽  
Chao Wang
Keyword(s):  
Systematic Review ◽  
3D Printing ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Technical Research ◽  
Research Status ◽  
Development Agenda ◽  
Future Research Directions ◽  
Future Topics

3D printing (3DP) is regarded as an innovation that contributes to automation in civil engineering and offers benefits in design, greenness, and efficiency. It is necessary to objectively analyze the current status and challenges associated with 3DP and identify future research directions to properly understand its construction applications. Previous research has focused more on the technical dimension of 3DP; however, the nontechnical dimension of the technology may hinder its implementation and thus must be paid particular attention to. This study presents a systematic review of the existing literature from both technical and nontechnical dimensions by combining quantitative and qualitative studies. The quantitative study was conducted using scientometric methods. The qualitative study analyzed information, including the technical research status and nontechnical challenges and trends. Two aspects of technical research status are presented, including materials and processes. In addition, nontechnical challenges and trends from the economic, environmental, social, and legislative aspects are proposed. This study provides a comprehensive agenda to advance 3DP in construction and proposes research interests, challenges, and future topics. It is intended to help construction practitioners systematically master existing processes and materials and assess the application degree and necessity of 3DP.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

Mass Customization with Configurable Products and Configurators

2011 ◽  
pp. 1-32 ◽  
Author(s):  
M. Heiskala
Keyword(s):  
Systematic Review ◽  
Information Systems ◽  
Mass Customization ◽  
Mass Production ◽  
Future Research ◽  
Customer Requirements ◽  
Research Directions ◽  
Customer Perspective ◽  
Future Research Directions ◽  
Configurable Products

Configurable products are an important way to achieve mass customization. A configurable product is designed once, and this design is used repetitively in the sales-delivery process to produce specifications of product individuals meeting customer requirements. Configurators are information systems that support the specification of product individuals and the creation and management of configuration knowledge, therefore being prime examples of information systems supporting mass customization. However, to the best of our knowledge, there is no systematic review of literature on how mass customization with configurable products and use of configurators affect companies. In this chapter, we provide such a review. We focus on benefits that can be gained and challenges which companies may face. A supplier can move to mass customization and configuration from mass production or from full customization; we keep the concerns separate. We also review benefits and challenges from the customer perspective. Finally, we identify future research directions and open challenges and problems.

scholarly journals Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

2020 ◽  
Vol 13 (3) ◽  
pp. 795-848
Author(s):  
Alina Köchling ◽  
Marius Claus Wehner
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Current Knowledge ◽  
Future Research ◽  
Journal Articles ◽  
Research Directions ◽  
Current State ◽  
Management Context ◽  
Future Research Directions ◽  
State Of Research

AbstractAlgorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.

Understanding the role of acculturative stress on refugee youth mental health: A systematic review and ecological approach to assessment and intervention

2019 ◽  
Vol 40 (2) ◽  
pp. 107-127 ◽  
Author(s):  
Ana d’Abreu ◽  
Sara Castro-Olivo ◽  
Sarah K. Ura
Keyword(s):  
Systematic Review ◽  
Acculturative Stress ◽  
Culturally Responsive ◽  
Refugee Children ◽  
Future Research ◽  
Assessment Practices ◽  
Research Directions ◽  
Refugee Youth ◽  
Future Research Directions

In this article, we conduct a systematic review of the extant literature on the risk and protective factors that impact the healthy resettlement of refugee children around the world. We identify acculturative stress as a main risk factor to consider for assessment and intervention given that is often overlooked in the literature for refugee children, but has been found to strongly impact their socio-emotional development. In addition, we discuss ecologically framed/culturally responsive interventions and assessment practices that could aid in the successful resettlement of refugee children. We also discuss the limitations of the extant research on refugee children and make recommendations for future research directions.

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