COMPLEXIS 2019 Abstracts

Area 1 - Complexity in Informatics, Automation and Networking

Full Papers

Paper Nr:	6
Title:	How Complex is to Solve a Hard Problem with Accepting Splicing Systems
Authors:	Victor Mitrana, Andrei Păun and Mihaela Păun
Abstract:	We define a variant of accepting splicing system that can be used as a problem solver. A condition for halting the computation on a given input as well as a condition for making a decision as soon as the computation has stopped is considered. An algorithm based on this accepting splicing system that solves a well-known NP-complete problem, namely the 3-colorability problem is presented. We discuss an efficient solution in terms of running time and additional resources (axioms, supplementary symbols, number of splicing rules. More precisely, for a given graph with n vertices and m edges, our solution runs in O(nm) time, and needs O(mn2) other resources. Two variants of this algorithm of a reduced time complexity at an exponential increase of the other resources are finally discussed.
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Short Papers

Paper Nr:	2
Title:	A Feasibility Study of a Method for Identification and Modelling of Cybersecurity Risks in the Context of Smart Power Grids
Authors:	Aida Omerovic, Hanne Vefsnmo, Gencer Erdogan, Oddbjørn Gjerde, Eivind Gramme and Stig Simonsen
Abstract:	Power grids are undergoing a digital transformation are therefore becoming increasingly complex. As a result of this they are also becoming vulnerable in new ways. With this development come also numerous risks. Cybersecurity is therefore becoming crucial for ensuring resilience of this infrastructure which is critical to safety of humans and societies. Risk analysis of cybersecurity in the context of smart power grids is, however, particularly demanding due to its interdisciplinary nature, including domains such as digital security, the energy domain, power networks, the numerous control systems involved, and the human in the loop. This poses special requirements to cybersecurity risk identification within smart power grids, which challenge the existing state-of-the-art. This paper proposes a customized four-step approach to identification and modelling of cybersecurity risks in the context of smart power grids. The aim is that the risk model can be presented to decision makers in a suitable interface, thereby serving as a useful support for planning, design and operation of smart power grids. The approach applied in this study is based on parts of the "CORAS" method for model-based risk analysis. The paper also reports on results and experiences from applying the approach in a realistic industrial case with a distribution system operator (DSO) responsible for hosting a pilot installation of the self-healing functionality within a power distribution grid. The evaluation indicates that the approach can be applied in a realistic setting to identify cybersecurity risks. The experiences from the case study moreover show that the presented approach is, to a large degree, well suited for its intended purpose, but it also points to areas in need for improvement and further evaluation.
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Paper Nr:	9
Title:	Real-time Processing of Rule-based Complex Event Queries for Tactical Moving Objects
Authors:	Yihuai Liang, Jiwan Lee, Bonghee Hong and Woochan Kim
Abstract:	Target data for tactical moving objects are streaming data collected in real time via radar, sonar, and other sensors. A system of continuous complex event query with dynamic rule definitions and high performance is needed to process that target data. We develop a continuous complex event query system with rule-based layered architecture. A continuous processing flow is decomposed into four modules hierarchically, which are event filtering, event capture, Continuous Queries (CQ) and Complex Event Processing (CEP). Each module has its responsibility but works together for a completed continuous processing flow. This paper shows that it is possible to dynamically insert, update, delete and search rule specifications of each layered modules through the decomposition of the whole system. Many rules are registered in the system for processing input event data continuously in real time. To improve the performance of getting matching CQs for each incoming event, CQ index is developed. Finally, experimentations and performance evaluations are carried out.
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Area 2 - Complexity in Social Sciences

Short Papers

Paper Nr:	12
Title:	An Analysis of Three Legal Citation Networks Derived from Austrian Supreme Court Decisions
Authors:	Markus Moser and Mark Strembeck
Abstract:	In this paper, we present a case study on the structural properties of three citations networks derived from Austrian Supreme Court decisions. In particular, we analyzed 250,984 Supreme Court decisions ranging from 1922 to 2017. As part of our case study, we analyzed the degree distributions, the structural properties of prominent court decisions, as well as changes in the frequency of legal citations over time.
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Paper Nr:	16
Title:	A Generalized Notion of Time for Modeling Temporal Networks
Authors:	Konstantin Kueffner and Mark Strembeck
Abstract:	Most approaches for modeling and analyzing temporal networks do not explicitly discuss the underlying notion of time. In this paper, we therefore introduce a generalized notion of time for temporal networks. Our approach also allows for considering non-deterministic time and incomplete data, two issues that are often found when analyzing data-sets extracted from online social networks, for example. In order to demonstrate the consequences of our generalized notion of time, we also discuss the implications for the computation of (shortest) temporal paths in temporal networks.
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Paper Nr:	18
Title:	A Social Inspired Broker for M2M Protocols
Authors:	Vincenza Carchiolo, Alessandro Longheu, Michele Malgeri and Giuseppe Mangioni
Abstract:	Internet of things can be viewed as the shifting from a network of computers to a network of things.To support M2M communication, several protocols have been developed; many of them are endorsed by client-broker model with a publish-subscribe interaction mechanism. In this paper we introduce a multi broker solution where the network of brokers is inspired by social relationships. This allow data sharing among several IoT systems, leads to a reliable and effective query forwarding algorithm and the small world effect coming from mimic humans relations guarantees fast responses and good query recall.
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Paper Nr:	21
Title:	IoT Forensics: A State-of-the-Art Review, Challenges and Future Directions
Authors:	Ahmed Alenezi, Hany F. Atlam, Reem Alsagri, Madini O. Alassafi and Gary B. Wills
Abstract:	The IoT is capable of communicating and connecting billions of things at the same time. The concept offers numerous benefits for consumers that alters how users interact with the technology. With this said, however, such monumental growth within IoT development also gives rise to a number of legal and technical challenges in the field of IoT forensics. Indeed, there exist many issues that must be overcome if effective IoT investigations are to be carried out. This paper presents a review of the IoT concept, digital forensics and the state-of-the-art on IoT forensics. Furthermore, an exploration of the possible solutions proposed in recent research and IoT forensics challenges that are identified in the current research literature are examined. Picks apart the challenges facing IoT forensics which have been established in recent literature. Overall, this paper draws attention to the obvious problems – open problems which require further efforts to be addressed properly.
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Area 3 - Complexity in Computational Intelligence and Future Information Systems

Short Papers

Paper Nr:	7
Title:	An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model
Authors:	Yuming Li, Pin Ni and Victor Chang
Abstract:	The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield.
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Paper Nr:	8
Title:	Using the Ornstein-Uhlenbeck Process for Random Exploration
Authors:	Johannes Nauta, Yara Khaluf and Pieter Simoens
Abstract:	In model-based Reinforcement Learning, an agent aims to learn a transition model between attainable states. Since the agent initially has zero knowledge of the transition model, it needs to resort to random exploration in order to learn the model. In this work, we demonstrate how the Ornstein-Uhlenbeck process can be used as a sampling scheme to generate exploratory Brownian motion in the absence of a transition model. Whereas current approaches rely on knowledge of the transition model to generate the steps of Brownian motion, the Ornstein-Uhlenbeck process does not. Additionally, the Ornstein-Uhlenbeck process naturally includes a drift term originating from a potential function. We show that this potential can be controlled by the agent itself, and allows executing non-equilibrium behavior such as ballistic motion or local trapping.
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Paper Nr:	11
Title:	The Time Operator of Reals
Authors:	Miloš Milovanović and Srđan Vukmirović
Abstract:	The purpose of the paper is to establish the continuum in terms of the complex systems physics. It is based upon the intuitionistic mathematics of Brouwer, implying a processual definition of real numbers that concerns the measurement problem. The Brouwerian continuum is proved to be a categorical skeleton of complex systems whose determining feature is the existence of time operator. Acting on continuous signals, the time operator represents multiresolution hierarchy of the measurement process. The wavelet domain hidden Markov model, that recapitulates statistical properties of the hierarchy, is applicable to a wide range of signals having experimental verification. It indicates a novel method that has already been proved tremendously useful in applied mathematics.
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Area 4 - Complexity in Risk and Predictive Modeling

Full Papers

Paper Nr:	1
Title:	Minimization of Attack Risk with Bayesian Detection Criteria
Authors:	Vaughn H. Standley, Frank G. Nuño and Jacob W. Sharpe
Abstract:	Strategic deterrence operates in and on a vast interstate network of rational actors seeking to minimize risk. Risk can be minimized by employing a likelihood ratio test (LRT) derived from Bayes’ Theorem. The LRT is comprised of prior, detection, and false-alarm probabilities. The power-law, known for its applicability to complex systems, has been used to model the distribution of combat fatalities. However, it cannot be used as a Bayesian prior for war when its area is unbounded. Analytics applied to Correlates of War data reveals that combat fatalities follow a log-gamma or log-normal probability distribution depending on a state’s escalation strategy. Results are used to show that nuclear war level fatalities pose increasing risk despite decreasing probability, that LRT-based decisions can minimize attack risk if an upper limit of impending fatalities is indicated by the detection system and commensurate with nominal false-alarm maximum, and that only successful defensive strategies are stable.
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