On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

Current Perspectives on Viral Disease Outbreaks

The COVID-19 pandemic has reminded the world that infectious diseases are still important. The last 40 years have experienced the emergence of new or resurging viral diseases such as AIDS, ebola, MERS, SARS, Zika, and others. These diseases display diverse epidemiologies ranging from sexual transmission to vector-borne transmission (or both, in the case of Zika). This book provides an overview of recent developments in the detection, monitoring, treatment, and control of several viral diseases that have caused recent epidemics or pandemics

Systems support for distributed learning environments

This thesis contends that the growing phenomena of multi-user networked "learning environments" should be treated as distributed interactive systems and that their developers should be aware of the systems and networks issues involved in their construction and maintenance. Such environments are henceforth referred to as distributed learning environments, or DLEs. Three major themes are identified as part of systems support: i) shared resource coherence in DLEs; ii) Quality of Service for the end- users of DLEs; and iii) the need for an integrating framework to develop, deploy and manage DLEs. The thesis reports on several distinct implementations and investigations that are each linked by one or more of those themes. Initially, responsiveness and coherence emerged as potentially conflicting requirements, and although a system was built that successfully resolved this conflict it proved difficult to move from the "clean room" conditions of a research project into a real world learning context. Accordingly, subsequent systems adopted a web-based approach to aid deployment in realistic settings. Indeed, production versions of these systems have been used extensively in credit-bearing modules in several Scottish Universities. Interactive responsiveness then emerged as a major Quality of Service issue in its own right, and motivated a series of investigations into the sources of delay, as experienced by end users of web-oriented distributed learning environments. Investigations into this issue provided insight into the nature of web-oriented interactive distributed learning and highlighted the need to be QoS-aware. As the volume and the range of usage of distributed learning applications increased the need for an integrating framework emerged. This required identifying and supporting a wide variety of educational resource types and also the key roles occupied by users of the system, such as tutors, students, supervisors, service providers, administrators, examiners. The thesis reports on the approaches taken and lessons learned from researching, designing and implementing systems which support distributed learning. As such, it constitutes a documented body of work that can inform the future design and deployment of distributed learning environments

A Study On Representing Cultural Heritage By Virtual and Augmented Reality

Bu çalışma, sanal ve güçlendirilmiş öğrenme yoluyla kültürel mirasın eğitimi, korunması ve sanal öğrenilmesine yönelik genel bir bakış sunmaktadır. Kültürel miras üzerine yapılan çalışmaların, sanal gerçeklik sunarak sanal bir ortamı çoğaltıp, görselleştirip temsil ederek, gerçek dünyadaymış hissi yaratma sorumlulukları vardır. Bu, kültürel mirasın öğrenme açısından önemli bir konu olduğunu ve öğrenmenin, sistemi geliştirenin ne istediğine değil, kullanıcının ne istediğine bağlı olduğunu ortaya koyar. Öte yandan, sanal ve güçlendirilmiş ortamların birçoğu, geliştirenin uygun gördüğü şeyden meydana gelmiş olsa da, öğrenme ortamı sunma ve kültürün korunması amaçlarına hizmet etmeye devam eder (Orta Avrupa Üniversitesi, 2017). Bunun nedeni, sanal ve artırılmış gerçekliğin, kişilerin kültürel ortama ulaşarak öğrenme deneyimi elde etmelerine yarayacak bir araç sunduğu gelişmiş teknoloji ortamında öğrenmenin gerçekleşebilecek olmasıdır. Ayrıca, sistem kullanıcılarının tam olarak neyi öğrenmek istediklerine ilişkin ihtiyaçlarının belirlenmesi ve sanal ortamın nasıl yaratıldığının görülmesi de, söz konusu sanal ortamın öğrenme ortamı sağlama konusundaki başarısının ortaya konulmasında önemlidir. Her ne kadar teknoloji, sanal ve artırılmış gerçekliğin kullanımını kolaylaştırmış olsa da, bu sanal ortamın kullandığı gelişmiş teknolojiye tüm kullanıcıların ulaşamıyor oluşu bir sorun teşkil etmektedir. Sanal ortam kullanıcılarının sanal ortamdaki objelerle etkileşime girme isteği de, sistem geliştiricilerinin çözmeleri gereken bir başka meseledir. Bu tezimde, sanal ve artırılmış gerçeklik ortamının kültürel açıdan eğitim, öğrenme ve koruma fırsatları sunulmasında önemli bir rolü olduğunu göstermeye çalıştım

Dynamics and collective phenomena of social systems

This thesis focuses on the study of social systems through methods of theoretical physics, in particular proceedings of statistical physics and complex systems, as well as mathematical tools like game theory and complex networks. There already ex- ists predictive and analysis methods to address these problems in sociology, but the contribution of physics provides new perspectives and complementary and powerful tools. This approach is particularly useful in problems involving stochastic aspects and nonlinear dynamics. The contribution of physics to social systems provides not only prediction procedures, but new insights, especially in the study of emergent properties that arise from holistic approaches. We study social systems by introducing different agent-based models (ABM). When possible, the models are analyzed using mathematical methods of physics, in order to achieve analytical solutions. In addition to a theoretical approach, experi- mental treatment is performed via computer simulations both through Monte Carlo methods and deterministic or mixed procedures. This working method has proved very fruitful for the study of several open problems. The book is structured as follows. This introduction presents the mathematical formalisms used in the investigations, which are structured in two parts: in part I we deal with the emergence of cooperation, while in part II we analyze cultural dynamics under the perspective of tolerance

Unravelling the many facets of human cooperation in an experimental study

Humans readily cooperate, even with strangers and without prospects of reciprocation. Despite thousands of studies, this finding is not well understood. Most studies focussed on a single aspect of cooperation and were conducted under anonymous conditions. However, cooperation is a multi-faceted phenomenon, involving generosity, readiness to share, fairness, trust, trustworthiness, and willingness to take cooperative risks. Here, we report findings of an experiment where subjects had to make decisions in ten situations representing different aspects of cooperation, both under anonymous and ‘personalised’ conditions. In an anonymous setting, we found considerable individual variation in each decision situation, while individuals were consistent both within and across situations. Prosocial tendencies such as generosity, trust, and trustworthiness were positively correlated, constituting a ‘cooperativeness syndrome’, but the tendency to punish non-cooperative individuals is not part of this syndrome. In a personalised setting, information on the appearance of the interaction partner systematically affected cooperation-related behaviour. Subjects were more cooperative toward interaction partners whose facial photographs were judged ‘generous’, ‘trustworthy’, ‘not greedy’, ‘happy’, ‘attractive’, and ‘not angry’ by a separate panel. However, individuals eliciting more cooperation were not more cooperative themselves in our experiment. Our study shows that a multi-faceted approach can reveal general behavioural tendencies underlying cooperation, but it also uncovers new puzzling features of human cooperation

Understanding the Intrinsic Motivations of User Acceptance of Hedonic Information Systems: Towards a Unified Research Model

Although user acceptance of entertainment-oriented information systems (IS), which are called Hedonic IS (HIS), has drawn considerable attention in literature, our understanding of user acceptance of HIS is still limited. This article focuses on exploring the intrinsic motivations of HIS acceptance from a unique perspective. It proposes a hybrid HIS acceptance model that considers the unique characteristics of HIS and the multiple conceptual identities of an HIS user. The model integrates intrinsic motivation factors from Hedonic theory, Flow theory, and the PAD (Pleasure, Arousal, and Dominance) emotion model with the Technology Acceptance Model. The proposed hybrid HIS acceptance model has been empirically tested by a quantitative field survey. The results indicate that emotional responses, imaginal responses, and flow experience are three main predictors of HIS acceptance

Performance and Reliability Evaluation of Apache Kafka Messaging System

Streaming data is now flowing across various devices and applications around us. This type of data means any unbounded, ever growing, infinite data set which is continuously generated by all kinds of sources. Examples include sensor data transmitted among different Internet of Things (IoT) devices, user activity records collected on websites and payment requests sent from mobile devices. In many application scenarios, streaming data needs to be processed in real-time because its value can be futile over time. A variety of stream processing systems have been developed in the last decade and are evolving to address rising challenges. A typical stream processing system consists of multiple processing nodes in the topology of a DAG (directed acyclic graph). To build real-time streaming data pipelines across those nodes, message middleware technology is widely applied. As a distributed messaging system with high durability and scalability, Apache Kafka has become very popular among modern companies. It ingests streaming data from upstream applications and store the data in its distributed cluster, which provides a fault-tolerant data source for stream processors. Therefore, Kafka plays a critical role to ensure the completeness, correctness and timeliness of streaming data delivery. However, it is impossible to meet all the user requirements in real-time cases with a simple and fixed data delivery strategy. In this thesis, we address the challenge of choosing a proper configuration to guarantee both performance and reliability of Kafka for complex streaming application scenarios. We investigate the features that have an impact on the performance and reliability metrics. We propose a queueing based prediction model to predict the performance metrics, including producer throughput and packet latency of Kafka. We define two reliability metrics, the probability of message loss and the probability of message duplication. We create an ANN model to predict these metrics given unstable network metrics like network delay and packet loss rate. To collect sufficient training data we build a Docker-based Kafka testbed with a fault injection module. We use a new quality-of-service metric, timely throughput to help us choosing proper batch size in Kafka. Based on this metric, we propose a dynamic configuration method, which reactively guarantees both performance and reliability of Kafka under complex operation conditions