A Study in Image Watermarking Schemes using Neural Networks

The digital watermarking technique, an effective way to protect image, has become the research focus on neural network. The purpose of this paper is to provide a brief study on broad theories and discuss the different types of neural networks for image watermarking. Most of the research interest image watermarking based on neural network in discrete wavelet transform or discrete cosine transform. Generally image watermarking based on neural network to solve the problem on to reduce the error, improve the rate of the learning, achieves goods imperceptibility and robustness. It will be useful for researches to implement effective image watermarking by using neural network

Measuring the Resilience of Transportation Networks Subject to Seismic Risk

Transportation networks are critical to the function of modern society but they are vulnerable to extreme events such as earthquakes. Damaged bridges can cost millions of dollars to repair and congestion and detours due to bridge closures leads to indirect costs that are even greater than the cost of repair of damaged bridges. A resilient network however should be able to limit the damage caused by earthquakes and recover in a timely fashion. Resilience of networks has been studying in length from a conceptual standpoint but as quantitative measure, the field has been lacking. This study sets forth to quantify resilience based on a set of performance measures and mapping them to the four properties of resilience: robustness, redundancy, resourcefulness and rapidity. The thesis ties in concepts from risk analysis that helps determine expected damage levels and connects those concepts to a resilience framework to better understand how a network responds and recovers after an earthquake. Also explored are methods to decrease repair time in order to limit the indirect costs due to network downtime as well as an overview of pre-event methods of improving resilience with a novel method of selecting bridges for retrofit while minimizing direct and indirect losses

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Characterising the security of power system topologies through a combined assessment of reliability, robustness, and resilience

Electricity has a prominent role in modern economies; therefore, ensuring the availability of electricity supply should be a top priority for policymakers. The joint assessment of reliability, robustness, and resilience can be a useful criterion to characterise different topologies and improve the security of supply. This paper proposes a novel integrated analysis of these three attributes to quantify the security of power grid topologies. Hence, eight case studies with different topologies created using the IEEE 24-bus reliability test system were analysed. Reliability was evaluated by applying the sequential Monte Carlo approach, robustness was evaluated by simulating cascading failures, and resilience was evaluated by analysing recovery curves. The different indicators associated with each of the three evaluations were then calculated. The results obtained were discussed both graphically and quantitatively in a novel three-dimensional representation, where the importance of joint analysis was also highlighted. The proposed method can serve as an additional tool for planners to identify possible investments or improvements in power system topologies

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure