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This document provides a comprehensive overview of the integration of civil structural health monitoring and machine learning in the field of concrete structures. It discusses the importance of monitoring infrastructure condition and the challenges of manual inspection. The document highlights the increasing use of machine learning algorithms, such as computer vision methods, in optimizing maintenance and repairs. It presents case studies and applications of machine learning in civil engineering, including damage detection and load assessment. The document also emphasizes the challenges and limitations of using machine learning in this context and recommends further research and interdisciplinary collaboration. Additionally, it addresses ethical and privacy concerns, the importance of open data sharing, and aligning machine learning applications with sustainability efforts. The document includes a list of academic articles related to machine learning techniques in concrete structures, covering topics such as crack detection and structural health monitoring. It also compiles various studies and research papers on machine learning for structural health monitoring in concrete structures, exploring different methods and highlighting the potential of deep learning models and image processing techniques for crack detection. Overall, the document provides a comprehensive overview of the application of machine learning in the field of structural health monitoring for concrete structures. [Extracted from the article]

Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of vehicle destruction. This paper presents a multi-agent deep reinforcement learning-based DR strategy (DRS) that optimizes the multi-UAV group redeployment in terms of swarm performance. To generate a two-layer DRS between multiple groups and a single group, a multi-agent deep reinforcement learning framework is developed in which a QMIX network determines the swarm redeployment, and each deep Q-network determines the single-group redeployment. The proposed method is simulated using Python and a case study demonstrates its effectiveness as a high-quality DRS for large-scale scenarios. [ABSTRACT FROM AUTHOR]

The proliferation of devices connected via the Internet of Things (IoT) leads to high energy consumption, e-waste production, and ultimately, additional extra carbon emissions. To maximize the benefits and reduce the detrimental impact of IoT on the environment, the concept of green IoT (GIoT) is presented, which can fulfill the need for a smart and sustainable world. This study aims to investigate the current practices, challenges, and policies related to the implementation of GIoT in Gulf Cooperation Council (GCC) countries (Saudi Arabia, United Arab Emirates (UAE), Qatar, Kuwait, Oman, and Bahrain). GCC countries are placed in the top 14 per capita carbon dioxide (CO 2 ) emissions in the world. This study pioneers discussing the GIoT through the GCC perspective. Therefore, this study reviews the threat of e-waste to the environment and counteract strategies of GCC nations. The impact of living standards in GCC countries on embodied energy and obsolescence overheads were highlighted. This study provides insights into economic diversification plans laid out by GCC countries in their national vision and moving away from dependence on hydrocarbon reserves. The renewable energy (RE) opportunities available in the context of smart city development and GIoT deployment were evaluated. Power consumptions of computing servers at data centers, sensor nodes, and machine-to-machine (M2M) communications were discussed. Integration of artificial intelligence in distributed computing environments was also studied. GCC countries lag in the network readiness index but rank higher on information and communications technology usage (ICT), individual skills, and access to ICT. GCC countries are also making steady progress in building the best international practices and in leveraging opportunities to transform digital government models. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous study introduced SMC to the reinforcement learning (RL) based on approximate dynamic programming in the context of optimal control, SMC is introduced to a conventional RL framework in this work. As a specific realization, the modified twin delayed deep deterministic policy gradient (DDPG) for consensus was exploited to develop sliding mode RL. Numerical experiments show that the sliding mode RL outperforms existing state-of-the-art RL methods and model-based methods in terms of the mean square error (MSE) performance. [ABSTRACT FROM AUTHOR]

Healthcare Janbi et al. [[10]] propose, implement, and evaluate Imtidad, a reference architecture that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over the cloud, fog, and edge. The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [[1]]. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) enable the generation of PPE models and the development of devices with more advanced capabilities such as monitoring, sensing the environment, and risk detection, among others. [Extracted from the article]

We are pleased to introduce a new issue of Mind, Culture and Activity, in which the journal's commitment with innovative interdisciplinary scholarship becomes tangible through the diversity of works included. These and other publications, as well as the possibilities of publishing materials and joining in ongoing dialogues, can be found at https://culturalpraxis.net Notes 1 https://culturalpraxis.net 2 https://culturalpraxis.net/coffee-hour-with-mike-cole-2/ 3 https://culturalpraxis.net/coffee-hour-with-andrew-coppens-2/ 4 https://culturalpraxis.net/coffee-hour-with-mariette-de-haan/ 5 https://culturalpraxis.net/imagination-and-creativity-in-vygotskys-works-seminar-series/ 6 https://culturalpraxis.net/%D1%81%D0%B8%D0%BC%D0%BF%D0%BE%D0%B7%D0%B8%D1%83%D0%BC-%D0%B2-%D0%BD%D0%BE%D0%B2%D0%BE%D1%81%D0%B8%D0%B1%D0%B8%D1%80%D1%81%D0%BA%D0%B5-%D0%B2%D0%BE%D0%B7%D1%80%D0%BE%D0%B6%D0%B4%D0%B5%D0%BD%D0%B8/ References 1 Cole, M. (1996). But sound, the authors argue, does more than mediate action: it also becomes an I affective atmosphere i , a notion the authors elaborate by drawing from human geography notions on spatiality. [Extracted from the article]

The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good. [ABSTRACT FROM AUTHOR]

Current work on multi-agent systems at King's College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning & Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation. [ABSTRACT FROM AUTHOR]

The article offers information on artificial-intelligence assisted dynamic modeling for data management in distributed system. Topics discussed include challenges in managing data and its protection which increases need for efficient information security management for systems, it discusses artificial intelligence-assisted dynamic modeling (AI-DM) used for data management in a distributive system; and that the suggested model provides users with links from anywhere to the distributed database.

Multi-Agent Systems (MASs) are the prominent symbol of Distributed Artificial Intelligence (DAI). Learning in MAS, which is commonly based on Reinforcement Learning (RL), is one of the problems that play an essential role in unknown environments. In this study aimed at solving the Multi-agent Credit Assignment (MCA) problem, we introduce the Task Start Threshold (TST) of agents as a new constraint in a multi-score operational environment, transforming the MCA into a bankruptcy problem. In the following, considering the bankruptcy concept, a new base algorithm, which is called Reverse Adjusted Proportional (RevAP), is introduced. Based on this algorithm, three methods PTST, T-MAS, and T-KAg, were presented to solve the MCA with different strategies. The proposed methods were evaluated in terms of group learning rate, confidence, expertness, certainty, efficiency, correctness, and density in comparison to the state-of-the-art methods such as ranking methods, dynamic, history-based as knowledge-based methods, Counterfactual Multi-Agent Policy Gradient (COMA) as an example of policy-based methods, Value-Decomposition Network (VDN) as an example of value-based methods, and Shapley Q- value Deep Deterministic Policy Gradient (SQDDPG) as a game theory-based method. The results reveal the better performance of the proposed approach compared to the existing methods based on the majority of the parameters. [ABSTRACT FROM AUTHOR]

This article presents iBuilding: distributed artificial intelligence embedded into Intelligent or Smart Buildings in an Industry 4.0 application that enables the adaptation to the external environment and the different building users. Buildings are becoming more intelligent in the way they monitor the usage of its assets, functionality and space. The more efficiently a building can be monitored or predicted, the more return of investment can deliver as unused space or energy can be redeveloped or commercialized, therefore reducing energy consumption while increasing functionality. This article proposes distributed artificial intelligence embedded into a Building based on neural networks with a deep learning structure. (1) Sensorial neurons at the device level are dispersed through the intelligent building to gather, filter environment information and predict its next values. (2) Management neurons based on reinforcement learning algorithm at the edge level make predictions about values and trends for building managers or developers to make commercial or operational informed decisions. (3) Finally, transmission neurons based on the genetic algorithms and the genome codify, transmit iBuilding information and also multiplex its data entirely to generate clusters of buildings interconnected with each other at the cloud level. The proposed iBuilding based on distributed learning is validated with a public research dataset; the results show that artificial intelligence embedded into the intelligent building enables real-time monitoring and successful predictions about its variables. The key concept proposed by this article is that the learned information obtained by iBuilding after its adaptation to the environment is never lost when the building changes over time or is decommissioned but transmitted to future generations. [ABSTRACT FROM AUTHOR]

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems. Most existing tracking solutions using online path planning focus on general target trajectories. Such trajectories have strong regularity and are smooth with minimal fluctuations. However, in real UAV applications a target will change its trajectory frequently to prevent from being tracked, and thus its trajectory will fluctuate greatly and randomly. To this end, in this paper we consider a realistic scenario where the UAV target trajectory is changeable and random. For real-time target tracking, we formulate the tracking task as a distributed model predictive control (DMPC) problem, the objective of which is to optimize tracking performance under various constraints. To do so, we innovatively combine the adaptive differential evolution (ADE) algorithm with Nash optimization, and then propose a Nash-combined ADE method. Specifically, we use ADE to adaptively adjust the predicted trajectory of each UAV agent and Nash optimization to efficiently solve DMPC formulation for global optimization, which can improve the tracking accuracy and simultaneously reduce the computational complexity. The simulation results show that the proposed method performs well in terms of tracking accuracy, UAV collision avoidance and tracking stability for realistic target trajectories. [ABSTRACT FROM AUTHOR]

The output feedback H∞ consensus control problem of multi‐agent systems is studied using an event‐triggered control strategy. Two types of transmission delays, one from the system output to the output feedback controller (OFC) and the other from the OFC to the zero‐order holder, are considered. This causes the OFC and the system not to be updated in the same time intervals. An interval dividing approach is applied to such that the whole system can be updated in the same time intervals. An event‐triggered OFC with H∞ performance is proposed for multi‐agent systems to achieve consensus. By constructing an appropriate Lyapunov–Krasovskii functional, sufficient conditions based on linear matrix inequality are derived to guarantee the consensus achievement. Finally, the theoretical results are validated using computer simulation. [ABSTRACT FROM AUTHOR]

How to enforce cooperation among unrelated agents is one of the main concerns in distributed artificial intelligence. The limited success of existing efforts to achieve large-scale cooperation is partly related to the lack of strong sanctioning mechanism to restrain defective agents who undermine cooperation. Here we use the framework of evolutionary game theory to investigate the emergence of cooperation in collective risk dilemma (CRD) through the implementation of instrumental ostracism on defective agents. By analyzing the stochastic model, we find that instrumental ostracism can better promote the emergence of widespread cooperation than punishment. Such advantage is more obvious when the risk is relatively low. Furthermore, we verify that the polycentric sanctioning institutions including punishment and ostracism are more effective than a single global one in improving the level of cooperation. Our model can be applied to many collective action problems, such as climate change and vaccination in Covid-19, so as to further provide some suggestions for decision makers. • We introduce instrumental ostracism into the collective-risk social dilemma game. • Instrumental ostracism can better promote the emergence of cooperation than punishment. • Polycentric institutions are more effective than global one in improving cooperation. [ABSTRACT FROM AUTHOR]

The integration of highly intermittent and unpredictable distributed energy resources increases the complexities in the energy management system (EMS). Therefore, the conventional control used in microgrid EMS is no more effective and requires alternative control strategies that can cope up with the new dynamics in the system. Adopted from computer science, a distributed artificial intelligence technique called multi agent system (MAS) can serve this purpose well due to its inherent autonomous nature, scalability, flexibility, and adaptability. Consequently, in this paper, a new multi agent based architecture for microgrid energy management system has been proposed. The proposed model considers different forecasting agents and a real time correction agent for microgrid energy management. The performance of proposed MAS based microgrid EMS modeled in a new platform called Stateflow has been evaluated by developing different scenarios considering resource fluctuations and load variations. The simulation results demonstrate the effectiveness of the proposed architecture in outperforming the conventional microgrid energy management system and the effective usage of each component of the microgrid. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) is emerging as a new and promising methodology with the development of Internet of Things (IoT) and smart devices, which enables a large class of applications and has emerged with a significance to change our life, and also is raising the challenging research issues due to the requirements of low latency, deployment independence, location awareness and mobility support. However, recent advances on distributed artificial intelligence (AI), big data, and edge/terminal cooperation schemes show that IoT still struggles with fundamental, long-standing problems, such as computational models, resource allocation and scheduling, power efficient data processing and cooperative decision-making mechanisms in edge. How to enhance IoT with MEC, distributed AI, cooperation and novel applications is worth exploring. The objective of this special issue is to report high-quality state-of-the-art research contributions that address these key aspects of MEC-enabled IoT and novel applications. By presenting a selection of papers on various topics related to MEC-enabled IoT, we hope to shed light on the multiple aspects of this emerging paradigm. The papers included in this issue propose solutions for resource management and allocation, multimedia processing, as well as novel applications. [ABSTRACT FROM AUTHOR]

Even simple artificial intelligence-enabled software agents can be adaptive and can implement transactions such as sending messages with a velocity and volume that far exceed the attention capacity of humans. Surprises could take the form of novel behaviour or inertia to planned change.[20] The more hidden or dark complexity there is in a system, the less likely we are to be able to predict the system's behaviour. Software agents are autonomous computer programmes that can independently do tasks, typically delegated to them by humans; a bot is a specific kind of agent that inhabits the internet The ability of software agents to mask their identity by assuming the outward attributes of other human or software agents. [Extracted from the article]

This paper is the editorial of the virtual special issue (VSI) "Artificial Intelligence for Distributed Smart Systems" (AI4DSS), of which the authors of this paper have been the guest editors. It aims to bring together the work of experts from the fields of artificial intelligence and that of smart sensing. Smart Sensing and, more generally, Smart Cyber Physical Systems are nowadays significantly impacting the everyday life of citizens and, in perspective, they will become pervasive in every aspect of human life from public health and well-being to home, infrastructures and environment management. Another important issue is related to the possibility of exploiting collaborative approaches through Distributed Architectures. In this kind of applications, smart sensors are spread into the environment of interest where some kind of "social intelligence" is generated. The papers included in this special issue allowed us to highlight the advances on this subject in three areas that are: Smart Cities, Smart Industries, Smart Healthcare. [ABSTRACT FROM AUTHOR]

We consider multi-label crowdsourcing learning in two scenarios. In the first scenario, we aim at inferring instances' groundtruth given the crowds' annotations. We propose two approaches NAM/RAM (Neighborhood/Relevance Aware Multi-label crowdsourcing) modeling the crowds' expertise and label correlations from different perspectives. Extended from single-label crowdsourcing methods, NAM models the crowds' expertise on individual labels, but based on the idea that for rational workers, their annotations for instances similar in the feature space should also be similar, NAM utilizes information from the feature space and incorporates the local influence of neighborhoods' annotations. Noting that the crowds tend to act in an effort-saving manner while labeling multiple labels, i.e., rather than carefully annotating every proper label, they would prefer scanning and tagging a few most relevant labels, RAM models the crowds' expertise as their ability to distinguish the relevance between label pairs. In the second scenario, we care about cost-efficient crowdsourcing where the labeling and learning process are conducted in tandem. We extend NAM/RAM to the active paradigm and propose instance, label, and worker selection criteria such that the labeling cost is significantly saved compared to passive learning without labeling control. The proposals' effectiveness are validated on simulated and real data. [ABSTRACT FROM AUTHOR]

With the crowdsourcing of small tasks becoming easier, it is possible to obtain non-expert/imperfect labels at low cost. With low-cost imperfect labeling, it is straightforward to collect multiple labels for the same data items. This paper proposes strategies of utilizing these multiple labels for supervised learning, based on two basic ideas: majority voting and pairing. We show several interesting results based on our experiments. (i) The strategies based on the majority voting idea work well under the situation where the certainty level is high. (ii) On the contrary, the pairing strategies are more preferable under the situation where the certainty level is low. (iii) Among the majority voting strategies, soft majority voting can reduce the bias and roughness, and perform better than majority voting. (iv) Pairing can completely avoid the bias by having both sides (potentially correct and incorrect/noisy information) considered. Beta estimation is applied to reduce the impact of the noise in pairing. Our experimental results show that pairing with Beta estimation always performs well under different certainty levels. (v) All strategies investigated are labeling quality agnostic strategies for real-world applications, and some of them perform better than or at least very close to the gnostic strategies. [ABSTRACT FROM AUTHOR]

The problem of crowdsourced entity collection solicits people (a.k.a. workers) to complete missing data in a database and has witnessed many applications in knowledge base completion and enterprise data collection. Although previous studies have attempted to address the "open world" challenge of crowdsourced entity collection, they do not pay much attention to the "distribution" of the collected entities. Evidently, in many real applications, users may have distribution requirements on the collected entities, e.g., even spatial distribution when collecting points-of-interest. In this paper, we study a new research problem, distribution-aware crowdsourced entity collection (CrowdDEC): Given an expected distribution w.r.t. an attribute (e.g., region or year), it aims to collect a set of entities via crowdsourcing and minimize the difference of the entity distribution from the expected distribution. Due to the openness of crowdsourcing, the CrowdDEC problem calls for effective crowdsourcing quality control. We propose an adaptive worker selection approach to address this problem. The approach estimates underlying entity distribution of workers on-the-fly based on the collected entities. Then, it adaptively selects the best set of workers that minimizes the difference from the expected distribution. Once workers submit their answers, it adjusts the estimation of workers' underlying distributions for subsequent adaptive worker selections. We prove the hardness of the problem, and develop effective estimation techniques as well as efficient worker selection algorithms to support this approach. We deployed the proposed approach on Amazon Mechanical Turk and the experimental results on two real datasets show that the approach achieves superiority on both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Abstract Over past few decades, swarm intelligent algorithms based on the intelligent behaviors of social creatures have been extensively studied and applied for all kinds of optimization areas. Teaching–learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of TLBO is divided into two phases and each phase executes iterative learning operation. Since its introduction by Rao and his team in 2010, TLBO has attracted more and more researchers' attention because of some of its strengths such as simple concept, without algorithm-specific parameters, rapid convergence and easy implementation yet effectiveness. In this paper we attempt to provide a brief review of the basic concepts of TLBO and a comprehensive survey of its prominent variants and its typical application, and the theoretical analysis conducted on TLBO so far. We hope that this survey can be very beneficial for the researchers engaged in the study of TLBO. [ABSTRACT FROM AUTHOR]

Proficient clustering method has a vital role in organizing sensor nodes in wireless sensor networks (WSNs), utilizing their energy resources efficiently and providing longevity to network. Hybrid energy-efficient distributed (HEED) protocol is one of the prominent clustering protocol in WSNs. However, it has few shortcomings, i.e., cluster heads (CHs) variation in consecutive rounds, more work load on CHs, uneven energy dissipation by sensor nodes, and formation of hot spots in network. By resolving these issues, one can enhance HEED capabilities to a greater extent. We have designed variants of Optimized HEED (OHEED) protocols named as HEED-1 Tier chaining (HEED1TC), HEED-2 Tier chaining (HEED2TC), ICHB-based OHEED-1 Tier chaining (ICOH1TC), ICHB-based OHEED-2 Tier chaining (ICOH2TC), ICHB-FL-based OHEED-1 Tier chaining (ICFLOH1TC), and ICHB-FL-based OHEED-2 Tier chaining (ICFLOH2TC) protocols. In HEED1TC and HEED2TC protocols, we have used chain-based intra-cluster and inter-cluster communication in HEED, respectively, for even load balancing among sensor nodes and to avoid more work load on CHs. Furthermore, for appropriate cluster formation, minimizing CHs variation in consecutive rounds and reducing complex uncertainties, we have used bacterial foraging optimization algorithm (BFOA)-inspired proposed intelligent CH selection based on BFOA (ICHB) algorithm for CH selection in ICOH1TC and ICOH2TC protocols. Likewise, in ICFLOH1TC and ICFLOH2TC protocols, we have used novel fuzzy set of rules additionally for CH selection to resolve the hot spots problem, proper CH selection covering whole network, and maximizing the network lifetime to a great extent. The simulation results showed that proposed OHEED protocols are able to handle above-discussed issues and provided far better results in comparison to HEED. [ABSTRACT FROM AUTHOR]

Highligths • Firefly Algorithm is extended as a multi-population based algorithm for dynamic optimization. • Quantum individuals are employed. • A simple but effective sub-swarm prioritizing strategy is proposed. • Firefly Algorithm is further enhanced and is referred to as Economical Firefly Algorithm. Abstract Optimization problems have attracted attention of researchers for decades. Commonly, problem related data and problem domain are assumed to be exactly known beforehand and to remain stationary. However, numerous real life optimization problems are dynamic. In practice, unpredictable events like due date changes, arrivals of new jobs or cancellations yield to changes in parameters, constraints or variables. In addition to the challenges of traditional stationary optimization problems, diverse parts of the problem space should also be monitored to keep track of moving optima in dynamic problems. Therefore, dividing a population (swarm) into smaller sized sub-swarms is a promising strategy particularly for multi-modal problems. In this context, the present work extends Firefly Algorithm (FA) as a multi-population based algorithm to solve multi-modal dynamic optimization problems due to its popularity and demonstrated competitive performance. Quantum particles are employed to monitor the neighborhoods of the best solutions of each sub-swarm in order to overcome the loss of diversity problem. Quantum strategy is also used to respond to dynamic events. Moreover, economical FA along with a simpler move function is introduced in order to consume fitness evaluations more efficiently. Most of the previous approaches ignore prioritizing sub-swarms which can be advantageous. For example, sub-swarms can either be evolved sequentially, randomly or they can be prioritized via some learning-based techniques. Thus, more promising regions might be discovered at earlier evaluations. In this context, the proposed FA extension is further enhanced with such prioritizing strategies, which are based on the feedback from sub-swarms. The experiments are conducted on the well-known Moving Peaks Benchmark along with comparisons with well-known methods. The proposed FA is found as promising and competitive according to the outcomes of the comprehensive experimental study. [ABSTRACT FROM AUTHOR]

In this work, we design distributed control laws for spatial self-organization of multiagent swarms in 1D and 2D spatial domains. The objective is to achieve a desired density distribution over a simply connected spatial domain. Since individual agents in a swarm are not themselves of interest and we are concerned only with the macroscopic objective, we view the network of agents in the swarm as a discrete approximation of a continuous medium and design control laws to shape the density distribution of the continuous medium. The key feature of this work is that the agents in the swarm do not have access to position information. Each individual agent is capable of measuring the current local density of agents and can communicate with its spatial neighbors. The network of agents implement a Laplacian-based distributed algorithm, which we call pseudo-localization, to localize themselves in a new coordinate frame, and a distributed control law to converge to the desired spatial density distribution. We start by studying self-organization in one dimension, which is then followed by the 2D case. [ABSTRACT FROM AUTHOR]

Blockchain has recently joined a long line of technological innovations that have been characterised as disruptive to, and possibly even subversive of, fundamental legal principles. This article looks behind the hype to examine how blockchain might – or might not – be compatible with established legal and regulatory models. Data protection is discussed as an example of an area of law that some have claimed cannot be reconciled with blockchain. Various other conflicts are also identified and concerns about blockchain are placed in the context of wider historical debates about new technologies vs law. [ABSTRACT FROM AUTHOR]

The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM. [ABSTRACT FROM AUTHOR]

Nowadays, along with the rapid development of information technologies, very often solving a common problem is entrusted to many autonomous units (people, systems). With such an approach, one can tap into the so-called collective intelligence (CI)—emerging from the collaboration and competition of many individuals. In this paper, we present recent research on CI related to the effectiveness of using the wisdom of crowds to perform a wide range of problems. For this aim, we first introduce a general framework of CI involving key characteristics of intelligent collectives. Next, we focus on the problem of how diversity and collective cardinality influence collective performance. Then, its applications, which are widely used such as prediction markets and Delphi method, will be presented. Furthermore, some research challenges on the capacity of combining CI with other research fields such as machine learning and social networks are also discussed. [ABSTRACT FROM AUTHOR]

This paper studies how to preserve connectivity for nonlinear time-delayed multiagent systems using event-based mechanism. By using the idea of divide-and-conquer, we divide the distributed controller into five parts to deal with different requirements of the time-delayed multiagent systems, such as eliminating the negative effects of time delays, preserving connectivity, learning the unknown dynamics and achieving consensus. To reduce the communication times among the agents, a centralized event-based protocol is introduced and an event-triggered function is devised to control the frequency of the communication without Zeno behavior. The technique of σ-functions is used to exclude the singularity of the established distributed controller. In the simulation example, the results demonstrate the validity of our developed methodology. [ABSTRACT FROM AUTHOR]

The demand of mobile and wireless devices has been continuously growing over the last years, driving the development of new wireless communication systems to provide high data capacity and good accessibility at high data transmission rates. Wireless communication systems must also show high immunity to interference, and in this context distributed systems formed by several remote antenna units (RAU) fed by multi-mode fibers are becoming the preferred solution. At high transmission data rate increases, issues brought by the variability in the fabrication of the circuits and ambient conditions (PVT variations) become more important, and it is necessary to design the systems to minimize their effect. In this work, a fully-differential transimpedance amplifier (TIA) for the receiver in a RAU is presented. The TIA has been designed in a 180-nm RF CMOS technology with a 1.8-V voltage supply and it achieves a bandwidth of 2.5 GHz with a power consumption of 26 mW. To overcome the effect of PVT variations, the TIA features a programmable transimpedance of 20-dB linear-in-dB programmable transimpedance, also increasing the input dynamic range to improve the overall linear range of operation. [ABSTRACT FROM AUTHOR]

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions. [ABSTRACT FROM AUTHOR]

A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In addition to proposing this methodology, three specific multi agent DRL approaches are considered: DRL-Independent, DRL Cooperative-Adaptive (CA), and DRL-Lenient. These approaches are validated and analyzed with an extensive empirical study using four different problems: 3D Mountain Car, SCARA Real-Time Trajectory Generation, Ball-Dribbling in humanoid soccer robotics, and Ball-Pushing using differential drive robots. The experimental validation provides evidence that DRL implementations show better performances and faster learning times than their centralized counterparts, while using less computational resources. DRL-Lenient and DRL-CA algorithms achieve the best final performances for the four tested problems, outperforming their DRL-Independent counterparts. Furthermore, the benefits of the DRL-Lenient and DRL-CA are more noticeable when the problem complexity increases and the centralized scheme becomes intractable given the available computational resources and training time. [ABSTRACT FROM AUTHOR]

Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. However, existing methods for answer aggregation are designed for discrete tasks, where answers are given as a single label per item. In this paper, we consider partial-agreement tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Common approaches for the aggregation of partial-agreement answers either (i) reduce the problem to several instances of an aggregation problem for discrete tasks or (ii) consider each label independently. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relationships in labels and different answer sets. We also show how this model is instantiated for incremental learning, incorporating new answers from crowd workers as they arrive. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and robustness against faulty workers and data sparsity. [ABSTRACT FROM AUTHOR]

Under the transition towards sustainable smart energy systems (SES), utilization of distributed intelligence has been gradually proposed along with the expansion of Information and Communication Technology (ICT) infrastructure and advanced control services. Distributed intelligence (DI)-based control and management solutions proved a perfect complement to the existing control structures to handle the SES’ uncertainty which is getting quite complex with different system layers and involved stakeholders. Advanced modelling and simulation techniques are crucial here to realize and enable the applications of DI to enhance grid reliability while optimize market operation. However, several challenges arise while modelling DI applications and integrating them in the simulation platform due to the complexity of the multi-disciplinary smart grids. As an activity of IEEE Task Force on Interfacing Techniques for Simulation Tools, this paper mainly reviews the interface issues between modelling and simulation of physical, ICT, and application layers, as well as business processes of the whole smart energy systems. By means of a conceptual framework for SES development, this paper aims to position most of DI-based control applications in specific research domain and elaborate on their interface with the whole SES context. [ABSTRACT FROM AUTHOR]

Robotic is a great substitute for human to explore the dangerous areas, and will also be a great help for disaster management. Although the rise of depth sensor technologies gives a huge boost to robotic vision research, traditional approaches cannot be applied to disaster-handling robots directly due to some limitations. In this paper, we focus on the 3D robotic perception, and propose a view-invariant Convolutional Neural Network (CNN) Model for scene understanding in disaster scenarios. The proposed system is highly distributed and parallel, which is of great help to improve the efficiency of network training. In our system, two individual CNNs are used to, respectively, propose objects from input data and classify their categories. We attempt to overcome the difficulties and restrictions caused by disasters using several specially-designed multi-task loss functions. The most significant advantage in our work is that the proposed method can learn a view-invariant feature with no requirement on RGB data, which is essential for harsh, disordered and changeable environments. Additionally, an effective optimization algorithm to accelerate the learning process is also included in our work. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks. [ABSTRACT FROM AUTHOR]

In this paper two swarm intelligence algorithms are used, the first leading the “attention” of the swarm and the latter responsible for the tracing mechanism. The attention mechanism is coordinated by agents of Stochastic Diffusion Search where they selectively attend to areas of a digital canvas (with line drawings) which contains (sharper) corners. Once the swarm's attention is drawn to the line of interest with a sharp corner, the corresponding line segment is fed into the tracing algorithm, Dispersive Flies Optimisation which “consumes” the input in order to generate a “swarmic sketch” of the input line. The sketching process is the result of the “flies” leaving traces of their movements on the digital canvas which are then revisited repeatedly in an attempt to re-sketch the traces they left. This cyclic process is then introduced in the context of autopoiesis, where the philosophical aspects of the autopoietic artist are discussed. The autopoetic artist is described in two modalities:gluttonousandcontented. In theGluttonous Autopoietic Artistmode, by iteratively focussing onareas-of-rich-complexity, as the decoding process of the input sketch unfolds, it leads to a less complex structure which ultimately results in an empty canvas; therein reifying the artwork's “death”. In theContented Autopoietic Artistmode, by refocussing the autopoietic artist's reflections on “meaning” onto different constitutive elements, and modifying her reconstitution, different behaviours of autopoietic creativity can be induced and therefore, the autopoietic processes become less likely to fade away and more open-ended in their creative endeavour. [ABSTRACT FROM PUBLISHER]

In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved. [ABSTRACT FROM AUTHOR]

The performance of the conventional Sigma-Delta modulator can be improved by cascading electrical integrator behind the mechanical sensing element. In this paper, a novel Sigma-Delta modulator with a fractional-order PIλDμ controller is proposed and thus extending the traditional Sigma-Delta modulator to a new fractional-order in the Sigma-Delta ADC design field. The fractional-order PIλDμ controller allows fine tuning accuracy in seeking the coefficients for the proposed Sigma-Delta modulator and provides the robust stability in the placement of poles and zeros for compensating the phase delays. The parameters of the proposed fractional-order Sigma-Delta modulator is designed by using swarm intelligent algorithm, which offers opportunity to simplify the process of tuning parameters and further improve the noise performance. Simulation from the proposed fractional-order Sigma-Delta modulator, results of SNR > 134 dB and the noise floor under -170 dB are obtained in frequency range of [5-150 Hz]. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants. [ABSTRACT FROM AUTHOR]

Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to providing numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality. [ABSTRACT FROM AUTHOR]