Improving the Performances of Asynchronous Search Algorithms in Scale-Free Networks Using the Nogood Processor Technique

The scale-free graphs were proposed as a generic and universal model of network topologies that exhibit power-law distributions in the connectivity of network nodes. In recent years various complex networks were identified as having a scale-free structure. Little research was done concerning the network structure for DisCSP, and in particular, for scale-free networks. The asynchronous searching techniques are characterized by the occurrence of nogood values during the search for a solution. In this article we analyze the distribution of nogood values to agents and the way how to use the information from the nogood; that is called the nogood processor technique. We examine the effect of nogood processor for networks that have a scale-free structure aiming to develop search algorithms specialized for scale-free networks of constraints, algorithms that require minimum costs for obtaining the solution. We develop a novel way for distributing nogood values to agents, thus obtaining a new hybrid search technique that uses the information from the stored nogoods. The experiments show that it is more effective for several families of asynchronous techniques; we perform tests with the model running on a cluster of computers. Also, we examine the effect of synchronization of agents' execution and of processing messages by packets in scale-free networks

Multi‑Agent Foraging: state‑of‑the‑art and research challenges

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic

Generating Strong Diversity of Opinions: Agent Models of Continuous Opinion Dynamics

Opinion dynamics is the study of how opinions in a group of individuals change over time. A goal of opinion dynamics modelers has long been to find a social science-based model that generates strong diversity -- smooth, stable, possibly multi-modal distributions of opinions. This research lays the foundations for and develops such a model. First, a taxonomy is developed to precisely describe agent schedules in an opinion dynamics model. The importance of scheduling is shown with applications to generalized forms of two models. Next, the meta-contrast influence field (MIF) model is defined. It is rooted in self-categorization theory and improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis. Finally, the MIF-Local Repulsion (MIF-LR) model is developed and presented. This augments the MIF model with a formulation of uniqueness theory. The MIF-LR model generates strong diversity. An application of the model shows that partisan polarization can be explained by increased non-local social ties enabled by communications technology

Individual-based artificial ecosystems for design and optimization

Individual-based modeling has gained popularity over the last decade, mainly due to the paradigm\u27s proven ability to address a variety of problems seen in many disciplines, including modeling complex systems from bottom-up, providing relationship between component level and system level parameters, and discovering the emergence of system-level behaviors from simple component level interactions. Availability of computational power to run simulation models with thousands to millions of agents is another driving force in the widespread adoption of individual-based modeling. This thesis proposes an individual-based modeling approach for solving engineering design and optimization problems using artificial ecosystems --Abstract, page iii

INTEREST-BASED FILTERING OF SOCIAL DATA IN DECENTRALIZED ONLINE SOCIAL NETWORKS

In Online Social Networks (OSNs) users are overwhelmed with huge amount of social data, most of which are irrelevant to their interest. Due to the fact that most current OSNs are centralized, people are forced to share their data with the site, in order to be able to share it with their friends, and thus they lose control over it. Decentralized Online Social Networks have been proposed as an alternative to traditional centralized ones (such as Facebook, Twitter, Google+, etc.) to deal with privacy problems and to allow users to maintain control over their data. This thesis presents a novel peer-to-peer architecture for decentralized OSN and a mechanism that allows each node to filter out irrelevant social data, while ensuring a level of serendipity (serendipitous are social data which are unexpected since they do not belong in the areas of interest of the user but are desirable since they are important or popular). The approach uses feedback from recipient users to construct a model of different areas of interest along the relationships between sender and receiver, which acts as a filter while propagating social data in this area of interest. The evaluation of the approach, using an Erlang simulation shows that it works according to the design specification: with the increasing number of social data passing through the network, the nodes learn to filter out irrelevant data, while serendipitous important data is able to pass through the network

Advanced Agent-Based Modeling for Social Networks

Agent-based modeling and simulation have been successfully applied to problems emerging from social sciences and could be profitably used also for the online social networks. However, the tools presently available for agent-based modeling do not offer specific support for social network models. In the present work, we present a unified conceptual framework to develop both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model to express the other models. In addition, we designed a domain-specific language to formulate the models in an executable way, so that simulations can be performed effortlessly. The language aims at being expressive and powerful for those with a strong background in computing, and yet simple and easy to learn for those with different expertises. We also developed a software platform that can execute such models in an agent-oriented context, providing effective support for large networks. Moreover, the platform hides most of the complexity of running the simulations on remote server-class machines. We validated out approach by translating several traditional models in our meta-model, verifying that the expected features of the models are maintained. The results show that our approach is successful in providing a friendly and easy environment to perform agent-based simulations over social networks, simulations that are of interest both to develop models and to study the results of the models themselves. Then, considering the favorable results we obtained, we applied our platform to the still open problem of creating an entirely distributed social networking system, which, as compared to the centralized ones, yields relevant advantages as far as privacy and resilience are concerned. We developed several models to help us in the understanding of the many issues that a P2P social networking system would have when deployed, and specifically of the well-known issue of the availability of rare resources. Through simulations, we found some criteria for the design of distributed social networks and some operation conditions which may result in a satisfactory user experience in terms of reduced delays in the propagation of information. Consequently, these results allow us to develop now a distributed social networking system optimized by means of our simulations

A Gamefied Synthetic Environment for Evaluation of Counter-Disinformation Solutions

This paper presents a simulation-based approach to countering online dis/misinformation. This disruptive technology experiment incorporated a synthetic environment component, based on adapted SIR epidemiological model to evaluate and visualize the effectiveness of suggested solutions to the issue. The participants in the simulation were given a realistic scenario depicting a dis/misinformation threat and were asked to select a number of solutions, described in IoS (Ideas-of-Systems) cards. During the event, the qualitative and quantitative characteristics of the IoS cards, were tested in a synthetic environment (SEN), built after a Susceptible-Infected-Resistant (SIR) model. The participants, divided into teams, presented and justified their dis/misinformation strategy which included three IoS card selections. A jury of subject matter experts, announced the winning team, based on the merits of the proposed strategies and the compatibility of the different cards, grouped together

Towards an Efficient, Scalable Stream Query Operator Framework for Representing and Analyzing Continuous Fields

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited. This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream query operator frameworks. First, the ST Interpolation Stream Query Operator Framework (STI-SQO framework) continuously transforms sensor streams into rasters using a novel set of stream query operators that perform ST-IDW interpolation. A key component of the STI-SQO framework is the 3D, main memory-based, ST Grid Index that enables high performance ST insertion and deletion of massive numbers of sensor observations through Isotropic Time Cell and Time Block-based partitioning. The ST Grid Index facilitates fast ST search for samples using ST shell-based neighborhood search templates, namely the Cylindrical Shell Template and Nested Shell Template. Furthermore, the framework contains the stream-based ST-IDW algorithms ST Shell and ST ak-Shell for high performance, parallel grid cell interpolation. Secondly, the proposed ST Predicate Stream Query Operator Framework (STP-SQO framework) efficiently evaluates value predicates over ST streams of ST continuous phenomena. The framework contains several stream-based predicate evaluation algorithms, including Region-Growing, Tile-based, and Phenomenon-Aware algorithms, that target predicate evaluation to regions with seed points and minimize the number of raster cells that are interpolated when evaluating value predicates. The performance of the proposed frameworks was assessed with regard to prediction accuracy of output results and runtime. The STI-SQO framework achieved a processing throughput of 250,000 observations in 2.5 s with a Normalized Root Mean Square Error under 0.19 using a 500×500 grid. The STP-SQO framework processed over 250,000 observations in under 0.25 s for predicate results covering less than 40% of the observation area, and the Scan Line Region Growing algorithm was consistently the fastest algorithm tested

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Self-Directed Learning

This book on self-directed learning (SDL) is devoted to original academic scholarship within the field of education, and is the 6th volume in the North-West University (NWU) SDL book series. In this book the authors explore how self-directed learning can be considered an imperative for education in a complex modern society. Although each chapter represents independent research in the field of self-directed learning, the chapters form a coherent contribution concerning the scholarship of self-directed learning, and specifically the effect of environmental and praxis contexts on the enhancement of self-directed learning in a complex society. The publication as a whole provides diverse perspectives on the importance of self-directed learning in varied contexts. Scholars working in a wide range of fields are drawn together in this scholarly work to present a comprehensive dialogue regarding self-directed learning and how this concept functions in a complex and dynamic higher education context. This book presents a combination of theory and practice, which reflects selected conceptual dimensions of self-directed learning in society, as well as research-based findings pertaining to current topical issues relating to implementing self-directed learning in the modern world. The varied methodologies provide the reader with different and balanced perspectives, as well as varied and innovative ideas on how to conduct research in the field of self-directed learning