Search

COVID-19 has hit hard on the global community, and organizations are working diligently to cope with the new norm of "work from home". However, the volume of remote work is unprecedented and creates opportunities for cyber attackers to penetrate home computers. Attackers have been leveraging websites with COVID-19 related names, dubbed COVID-19 themed malicious websites. These websites mostly contain false information, fake forms, fraudulent payments, scams, or malicious payloads to steal sensitive information or infect victims' computers. In this paper, we present a data-driven study on characterizing and detecting COVID-19 themed malicious websites. Our characterization study shows that attackers are agile and are deceptively crafty in designing geolocation targeted websites, often leveraging popular domain registrars and top-level domains. Our detection study shows that the Random Forest classifier can detect COVID-19 themed malicious websites based on the lexical and WHOIS features defined in this paper, achieving a 98% accuracy and 2.7% false-positive rate., Comment: 6 pages, 5 figures, manuscript published in 2020 IEEE International Conference on Intelligence and Security Informatics (ISI 2020)

Reproducibility in biomedical research, and more specifically in preclinical animal research, has been seriously questioned. Several cases of spectacular failures to replicate findings published in the primary scientific literature have led to a perceived reproducibility crisis. Diverse threats to reproducibility have been proposed, including lack of scientific rigour, low statistical power, publication bias, analytical flexibility and fraud. An important aspect that is generally overlooked is the lack of external validity caused by rigorous standardization of both the animals and the environment. Here, we argue that a reaction norm approach to phenotypic variation, acknowledging gene-by-environment interactions, can help us seeing reproducibility of animal experiments in a new light. We illustrate how dominating environmental effects can affect inference and effect size estimates of studies and how elimination of dominant factors through standardization affects the nature of the expected phenotype variation through the reaction norms of small effect. Finally, we discuss the consequences of reaction norms of small effect for statistical analysis, specifically for random effect latent variable models and the random lab model.

This chapter briefly introduces NORM (No-ORM) - a methodology of application development that avoids many of pitfals of widely accepted development patterns. Within NORM, the interactions between application code and the database are restricted by a contract that limits the number of interactions and ensures high performance. To address the consequences of impedance mismatch, an additional model, called transfer model, is mapped to both the database model and the application model, providing means for interactions in terms of high-level complex data objects.

Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies–Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score.

The emergence of connected and autonomous vehicles is at a rapid and unprecedented pace. A recent state-sponsored initiative calls for building a transportation information network that utilizes intelligent sensors on sections of state highways. Indeed, vehicle to infrastructure communication systems are slowly evolving. Peripheral sensors that are used to assist the human operator in lane changing, obstacle avoidance, and parking is becoming a norm in modern automotive vehicles. Although this newly found convenience is a boon to society, both socially and economically, it presents security challenges that are endemic to connected technologies. These challenges underscore the need to look closely at the state of automotive vehicle security. In conjunction with this effort, it is imperative that we investigate the metrics with which security can be measured. As a major component of continuous improvement, quantitative and qualitative measures must be devised to be able to make a full appreciation of the process. This chapter describes automotive vehicle security metrics and derives sample attack calculations to illustrate their applicability.

The Automotive SPICE for Cybersecurity Assessment Model has been published as a first version in February 2021. It will be used for cybersecurity homologation assessments of projects. A new UNECE norm requires from summer 2022 onwards the presence of a CSMS (Cybersecurity Management System) for organisations and the VDA AK13 published in Feb 2021 a new assessment model for the cybersecurity of Automotive development projects. The SOQRATES group as a working party of Automotive suppliers and research and training bodies has developed a cybersecurity engineer job role and training and best practice examples of how to cover the new security engineering practices. Also, first trials of using the new security related questions have been done and first experiences are shared in this paper.

With the world reeling under the COVID-19 pandemic, maintaining distance from fellow humans is the new social interaction norm. During these unprecedented times, the medical fraternity and health workers are in dire need of additional help. Robots have been deployed for quite some time for facilitating industrial and healthcare needs. This paper tries to highlight the gap between affordable robots available for health institutions and the demand for them. The aim of this article is to design a prototype robot that can deliver food and medical trays to COVID-19 patients in hospitals to restrict human interaction and hence preventing the further spread of the disease. Autodesk Fusion 360, a computer-aided design tool, has been extensively used to design the robot.

With the advent of evolution of cloud computing, large organizations have been scaling the on-premise IT infrastructure to the cloud. Although this being a popular practice, it lacks comprehensive efforts to study the aspects of automated negotiation of resources among cloud customers and providers. This paper proposes a full-fledged framework for the multi-party, multi-issue negotiation system for cloud resources. It introduces a robust cloud marketplace system to buy and sell cloud resources. The belief–desire–intention (BDI) model-based cloud customer and provider agents concurrently negotiate on multiple issues, pursuing a hybrid tactic of time- and resource-based dynamic deadline algorithms to generate offers and counter-offers. The cloud marketplace-based system is further augmented with the assignment of behavior norm score and Reputation Index to the agents to establish trust among them.

Recently, a great attention is paid toward developing aggregation operators for Pythagorean fuzzy set (PFS). However, few of them have adopted the rules of Archimedean t-conorm and t-norm (ATT) to aggregate the numbers. Motivated by this, the keep interest of the present work is to define some Pythagorean fuzzy interaction aggregation operators with the aid of ATT. To do this, the objective of the work is divided into three folds. In the first fold, we define some interactive operations law for PFSs and propose their corresponding new interaction Pythagorean operators, namely Archimedean based Pythagorean fuzzy interactive weighted averaging (A-PFIWA) operator and Archimedean based Pythagorean fuzzy interactive weighted geometric (A-PFIWG) operator. In the second fold, we investigate their desirable properties and study the several special cases of the proposed ones with respect the existing ones. Lastly, we design an algorithm to solve the multiple attribute decision-making issues with Pythagorean fuzzy uncertainties and explain their utilization with a numerical example. Further, several examples are discussed to demonstrate the validity and superiority of the proposed method. The novelties of proposed operators are that they not only can offer a more flexible method via selecting diverse forms of ATT, but also can reflect the interactive influence among membership degree and nonmembership degree for PFSs.

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. Directly transmitting data for centralized learning will cause long communication latency and may incur severe privacy issue as well. To address these issues, we consider the importance-aware federated edge learning (FEEL) system in this paper. Based on the relation between loss decay and gradient norm, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Finally, the test results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms.

Automakers keep introducing new functions to vehicles to entice customers, thus increasing the size/number of in-vehicle buses. As a result, adopting multi-buses with a central gateway is becoming a norm in current and future vehicles. Since adding buses increases production cost, knowing whether or not to add a bus at design time to meet the given requirements is essential to design a cost-optimized in-vehicle network. However, due to the lack of an optimal priority-assignment algorithm for multi-bus systems, it is difficult to determine whether additional buses are needed. To address this difficulty, we propose an optimal priority-assignment algorithm, called OPMB, for multiple CAN/CAN-FD buses with a central gateway. OPMB builds on backtracking, and is thus of exponential time complexity. To reduce the execution time effectively for industry-size problems, we identify several theory-proven search-space reduction conditions. Our in-depth simulation has demonstrated that OPMB outperforms the state-of-art priority-assignment algorithms for multi-bus systems, and is suitable for high-speed systems which represent future automotive systems. Also, OPMB is shown to be feasible for most realistic automotive message sets.

Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements are found for our adversarial models, which are further improved by the application of dual-batch normalization. Contrary to previous research on adversarially trained models, we find that accuracy of such models is equal to standard models, when sufficiently large datasets and dual batch norm training are used. To ensure transferability, we additionally validate our results on an external test set of 22,433 X-rays. These findings elucidate that different paths for adversarial and real images are needed during training to achieve state of the art results with superior clinical interpretability., Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, the authors demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts.

This chapter considers three enduring nuclear challenges that will shape our near-term nuclear future. First, how we can safely and securely manage the hundreds of civilian nuclear power and research facilities across the globe in order to ensure against the threat of weapons proliferation. Second, whether the nuclear taboo and norm of non-nuclear use that appears to have characterised our nuclear past can continue to hold into the future. Third, it considers to what extent new nuclear and non-nuclear weapon technologies and a technology-driven shift in the global nuclear environment will have a disruptive and destabilising impact on the global nuclear order.

Following the CENELEC railway standards for development of safe railway control and protection systems, their generic safety case and its technical safety report will be analyzed and applied in detail for a satellite-based train localization system. A norm compliant qualification procedure and a collection for both the GNSS localization's and other sensor component's performance values as well as for the localization system build is defined.

Last decade has been characterized by a huge advancement in the field of automated and connected transport. However, fully autonomous systems still need a lot of effort in order to be applied in transportation. Meanwhile, mixed traffic environments with semi-autonomous vehicles is becoming a norm. In such conditions, vehicles are passing the dynamic driving task back to the human by sending to drivers Requests to Intervene (RtI). At the same time, there is a need to evolve driver’s training in order to be able to safely use semi-automated vehicles, whereas driver intervention performance has to be made an integral part of both driver and technology assessment. Furthermore, the ethical implications of automated decision-making need to be properly assessed, giving rise to novel risk and liability analysis models. In this conceptual paper we present our vision to maximise the safety, trust and acceptance of automated vehicles. To achieve that, we propose an assessment framework to evaluate different technologies involved in Automated Driving Systems (ADS).

The IR 4.0 and automated construction progress detection are greenfield areas among researchers in current times. However, the implementation of the IR 4.0 theme for progress detection technologies needs special considerations as an emerging concept. This study aims to understand and develop a theoretical framework for IR 4.0 operational through the machine learning (ML) integrated towards automated construction progress detection and data acquisition technologies. Therefore, the detailed literature reviews were conducted in reference to construction progress detection technologies, with machine learning (ML) integrated techniques within IR 4.0 norm. Based on the literature outcomes, the theoretical framework was designed for the ML integrated project progress detection technologies. The designed IR 4.0 framework emphasises the overall effectiveness and efficiency of the monitoring operations. Moreover, it also highlights the challenges to overcome, such as financial impacts of technological adoption, interoperability issues between technologies etc. It has been concluded that there is a need for the development of field-based experimented IR 4.0 automated progress detection for the effective implementation of technologies.

The system structure and method for long-term continuous monitoring and control of patient's health state are considered. In order to build such systems and provide biomedical research outside of a medical institution the following problems have to be solved: to form a complex of diagnostically significant indicators for health state abnormality detection; to develop methods of biomedical signals acquisition at patient's vital activity as well as algorithms of signals processing and indicators assessment. It is shown that these systems should operate in the intelligent monitoring mode. It involves a change in the number of active channels for picking up and recording of biomedical signals, and their characteristics, the number of signs used to identify episodes of exacerbation of a chronic disease, when diagnostically significant indicators of the disease go beyond the patient's individual norm.

Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. Transliterating those languages improves very significantly the ability of large-scale multilingual language models on downstream tasks., Accepted at NAACL-HLT 2021

This paper presents a review on various agricultural practices and aspects that can be or currently are automated, using robotics, IoT and Artificial Intelligence (AI) more prolifically. Alongside, the current and future perspectives are dealt with, covering major technology innovations focused around smart farming, precision agriculture, vertical farming, modern greenhouse practices, autonomous and robotic workforce, drones, and the ‘connected farm.’ Post Covid-19, automation of agricultural industry has become all the more relevant to the new norm set due to labour migration and shortage.

In this chapter, I provide a formal machinery to complement the epistemic background in Davidson’s triangulation argument by presenting a structural modelling of the argument in the framework of an epistemic logic of actions. I focus on two aspects. Firstly, to guarantee that the propositional content of what the speaker says in the ongoing communication is justified true beliefs, I suggest that the speaker’s utterances must be assertions governed by the knowledge norm, assuming Davidson’s characterization of knowledge in terms of holistically justified true beliefs. Secondly, I claim that ongoing communication succeeds only if what the speaker says becomes common knowledge. Accordingly, the required underlying system for the proposed logic of actions will be a logic of common knowledge, in which the notion of common knowledge is characterized in terms of the knowledge account of assertion. A semantics appropriate for the logic of common knowledge will be given. I further propose that the transition from the speaker’s initial assertion to common knowledge can be illuminated by virtue of individual agents’ learning and the group learning, resulting from what the hearer learns from the speaker’s assertion and what the speaker learns from the hearer’s response, and the following interactions between them. Two act-operators for individual agents’ learning and group learning are introduced; the required semantic rules are stipulated.

Combining the blockchain and cloud computing systems in different ways in order to address computing issues such as decentralization and security has slowly become a norm in recent times. These Block-Cloud model(s), which are becoming prominent in the literature seem to ensure integrity in the Cloud but lacks in exhibiting availability of resources, which is essential for security. In this study, a dew-blockcloud model is designed to address this problem. We propose a hybrid MRU/FRU caching algorithm for the dew server that reduces the number of repeated cloud accesses while maintaining relevant updated data. Using existing literature and postulation we show that dew computing might be the best availability system for the blockcloud model.

The COVID-19 pandemic dictated that wearing face masks during public interactions was the new norm across much of the globe. As the masks naturally occlude part of the wearer's face, the part of communication that occurs through facial expressions is lost, and could reduce acceptance of mask wear. To address the issue, we created 2 face mask prototypes, incorporating simple expressive display elements and evaluated them in a user study. Aiming to explore the potential for low-cost solutions, suitable for large-scale deployment, our concepts utilized bi-state electrochromic displays. One concept Mouthy Mask aimed to reproduce the image of the wearer's mouth, whilst the Smiley Mask was symbolic in nature. The smart face masks were considered useful in public contexts to support short socially expected rituals. Generally a visualization directly representing the wearer's mouth was preferred to an emoji style visualization. As a contribution, our work presents a stepping stone towards productizable solutions for smart face masks that potentially increase the acceptability of face mask wear in public.

Motivation The analysis of gene co-expression network (GCN) is critical in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. Numerous clustering methods have been developed to detect communities of co-expressed genes in the large network. The assumed independent community structure, however, can be oversimplified and may not adequately characterize the complex biological processes. Results We develop a new computational package to extract interconnected communities from gene co-expression network. We consider a pair of communities be interconnected if a subset of genes from one community is correlated with a subset of genes from another community. The interconnected community structure is more flexible and provides a better fit to the empirical co-expression matrix. To overcome the computational challenges, we develop efficient algorithms by leveraging advanced graph norm shrinkage approach. We validate and show the advantage of our method by extensive simulation studies. We then apply our interconnected community detection method to an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study and identify essential interacting biological pathways related to the immune evasion mechanism of tumor cells. Availabilityand implementation The software is available at Github: https://github.com/qwu1221/ICN and Figshare: https://figshare.com/articles/software/ICN-package/13229093. Supplementary information Supplementary data are available at Bioinformatics online.

Talking to the electronic device and getting the required information at a minimal time has become today's norm. Although AI-powered conversational agents have percolated the commercial market, their use in a communal setting is still evolving. We postulate that the deployments of chatbots in disaster-prone areas can be beneficial to watch, monitor, and warn people during the crisis. Furthermore, the successful implementation of such technology can be life-saving. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques.

Adversarial examples are theorized to exist for every type of neural network application. Adversarial examples have been proven to exist in neural networks for visual-spectrum applications and that they are highly transferable between such neural network applications. In this paper, we study the existence of adversarial examples for Infrared neural networks that are applicable to military and surveillance applications. This paper specifically studies the effectiveness of adversarial attacks against neural networks trained on simulated Infrared imagery and the effectiveness of adversarial training. Our research demonstrates the effectiveness of adversarial attacks on neural networks trained on Infrared imagery, something that hasn&rsquo, t been shown in prior works. Our research shows that an increase in accuracy was shown in both adversarial and unperturbed Infrared images after adversarial training. Adversarial training optimized for the L&infin, norm leads to an increase in performance against both adversarial and non-adversarial targets.

This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

This paper presents the basic principles of seismic analysis of structures according to the YU81 andEuropean norm EN 1998-1: 2004. The aim of the paper is a critical review of comparative analysisof seismic impacts in the design of building structures according to these standards. EC8 involvesseveral innovative approaches for the design and construction of structures, such as no structuralfailure, limiting the degree of structural damage, important public use facilities must remain usable.Purpose of this research is to compare the value of seismic force according to YU81 and EC8, forthe same type of structure, depending on the parameters variation, such as different soil categories,different seismicity class, different building structural systems.

Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID). Specifically, it requires to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. One major challenge comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two sub-tasks in a joint person search model. To this end, We present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by mis-alignment. We outperform other one-step methods by a large margin and achieve comparable performance to two-step methods on both CUHK-SYSU and PRW. Also, Our method is easy to train and resource-friendly, running at 12 fps on a single GPU.

Throughout the history of humanity, the way that humans transmit intelligence from generation to generation has changed multiple times. Beginning verbally and through manuscripts, continuing with patented inventions, official and private documents, nowadays, the different ways of adapting and implementing the knowledge acquired through data are being highlighted. Whether with regards to human, artificial, or mixed intelligence, data can provide consistent and meaningful answers to address the challenges of today's businesses. This scientific paper contributes to the vision of a hybrid human and artificial intelligence approach, thus explaining, exemplifying, and presenting research on how today's organizations apply the concept of data efficiency and effectiveness from a business intelligence perspective. The fact that decision-makers can be more performant with the help of data science and machine learning has the power of unlocking strengths and opportunities at an unprecedented rate and therefor is the new norm in the modern business world.

Software is eating the world. To do their jobs well, executives and managers outside of technology will benefit from understanding some fundamentals of software and the software-delivery process.

Complexity measures allow for reflecting on critical issues and estimating the efficiency of various processes and systems. To better organize the functioning of the legal domain, it is rational to use complexity measures. In essence, any legal process is an information process in the legal domain, involving one or several legal systems. Thus, the goal of this work is the development of theoretical and practical tools for the estimation of complexity of information processes, that is, complexity measures in the legal domain, with the goal to ameliorate functioning of the legal systems.

This this paper positions cybersecurity capacity building in the realm of the debate on international norms. It is argued that cybersecurity capacity building on the one side is a necessity to make...

This article argues that the nuclear nonproliferation norm (NNPN) is a social fact with a relatively independent life of its own and that it has a powerful impact on the behavior of both nuclear-we...

This paper focuses on presenting a conceptual model in order to integrate sustainability in cloud computing resulting in facilitating transmission of knowledge in education with a focus on developing countries. From one perspective and while cloud computing has been considered as a recent area of research due to arguments round its value to organizational performance, sustainability has turned to be a typical common norm due to the discovery of uncertainty in various human aspects. From a different perspective, the role of Knowledge Management in enhancing organisational success has been detected in different researches. The main consensus after these researches is that the more effective management of knowledge is the more effective the organizational performance will be. Therefore, this paper presents a theoretical framework of potential links among cloud computing, knowledge management and sustainability.

Biogas plays an important role in renewable energy production. Biogas plants are small-scale energy plants, facing numerous risks in daily operations. Production of biogas may cause danger for life and health by suffocation or poisoning due to presence of hydrogen sulphide (H2 S) and explosion of reactor. The current literature study revealed that there are no universal and comprehensive management tools designed only for biogas plants for risk management. Therefore, the aim of this paper is application of new ISO 31000:2018 on risk management in biogas plants. For this, three crucial technical criteria: Organic loading rate (OLR), Hydraulic retention time (HRT) and Technical performance of a biogas plant were taken into account. These criteria have been analysed to present how the methodology of risk management norm can be applied in any other area. Further research is needed to develop additional criteria in technical, environmental, economic and social issues.

Radioactive related pollution due to suspended particulate matter dispersion is an important workplace and health care issue. Recycling oil production ducts and contaminated production equipment, represent a health hazard to workers and public alike. Radioactive plate-out NORM scales with crystal deposit is analyzed by different techniques; results provide proper information on physico-chemical features and emitted alpha particles. Recommendations for handling and recycling procedures are included in relation to health risk and radiological hazard.

Given the advances made in conventional weapon capabilities, precision should by now be the accepted and expected norm in cyberspace as well. In this article I argue that developing precision cyber...

We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents–the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state.

Tort cases apply particular standards of behaviour to defendants and society, often using common-sense sounding benchmarks such as “reasonableness”. This article explores the ways in which courts establish facts, for the purpose of making a decision on appropriate standards of behaviour, by using sources of authority from the world beyond tort. Facts, rather than being separate from and prior to the application of the legal norms, are often inseparably bound with legal judgment in any particular decision. Two areas of tort law are assessed: some of the English asbestos cases, and the Dutch Urgenda climate change case. The facts required for the setting of, and compliance with, the standard of negligence in these cases are found in a range of external sources, including standards, rules and understandings from scientific bodies, regulators, legal and non-legal documents.

The knowledge norm of assertion is the subject of a lively debate on when someone is in a position to assert something. However, not much has been said about the logic that underlies such debate. In this paper, I propose a formalisation of the knowledge norm in a deontic logic that aims to be explanatory and conceptually sound. Afterwards, I investigate some problems that this formalisation makes visible. This reveals some significant limitations of the underlying logic: it can neither contain Axiom 4 (transitivity) nor Axiom C4 (density). Moreover, sentences of the form p and I have not asserted that p appear to licence a violation of deontic rules.

In the context of granular computing, sequential three-way decision is a useful tool to triadic thinking, triadic computing and triadic processing from coarser to finer under multilevel and multiview granularity space. In this paper, we mainly explore a novel framework of sequential three-way decision for the fusion of mixed data from the subjective and objective dynamic perspectives. The former focuses on the decision maker’s dynamic behavior without considering the time-evolving data, and the latter emphasizes on dealing with dynamic mixed data over time by multi-stage decision-making. We firstly utilize four T -norm operators and kernel-based similarity relations to integrate different types of dynamic data. Then the subjective and objective models of sequential three-way decision are investigated based on decision thresholds, attribute importance and cost reduction. Finally, the comparative experiments are reported to verify that our proposed models can achieve the lower decision cost and the acceptable accuracy.