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Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that pragmatic tools for reasoning with uncertainty lag behind this rich body of theoretical results. Rough set theory is one such approach for modeling incompleteness and imprecision based on indiscernibility and its generalizations. In this paper, we provide a pragmatic tool for constructively reasoning with generalized rough set approximations that is based on the use of Answer Set Programming (Asp). We provide an interpretation of answer sets as (generalized) approximations of crisp sets (when possible) and show how to use Asp solvers as a tool for reasoning about (generalized) rough set approximations situated in realistic knowledge bases. The paper includes generic Asp templates for doing this and also provides a case study showing how these techniques can be used to generate reducts for incomplete information systems. Complete, ready to run clingo Asp code is provided in the Appendix, for all programs considered. These can be executed for validation purposes in the clingo Asp solver. Funding: ELLIIT Network Organization for Information and Communication Technology, Sweden; Swedish Foundation for Strategic Research SSF(Smart Systems Project) [RIT15-0097]; Jinan University (Zhuhai Campus); National Science Centre PolandNational Science Centre, Poland [2017/27/B/ST6/02018] ELLIIT Smart Systems Project RIT15-0097

Constrained Application Protocol (CoAP) has become the de-facto web standard for the IoT. Unlike traditional wireless sensor networks, Internet-connected smart thing deployments require security. CoAP mandates the use of the Datagram TLS (DTLS) protocol as the underlying secure communication protocol. In this paper we implement DTLS-protected secure CoAP for both resource-constrained IoT devices and a cloud backend and evaluate all three security modes (pre-shared key, raw-public key, and certificate-based) of CoAP in a real cloud-connected IoT setup. We extend SicsthSense– a cloud platform for the IoT– with secure CoAP capabilities, and compliment a DTLS implementation for resource-constrained IoT devices with raw-public key and certificate-based asymmetric cryptography. To the best of our knowledge, this is the first effort toward providing end-to-end secure communication between resource-constrained smart things and cloud back-ends which supports all three security modes of CoAP both on the client side and the server side. SecureSense– our End-to-End (E2E) secure communication architecture for the IoT– consists of all standard-based protocols, and implementation of these protocols are open source and BSD-licensed. The SecureSense evaluation benchmarks and open source and open license implementation make it possible for future IoT product and service providers to account for security overhead while using all standardized protocols and while ensuring interoperability among different vendors. The core contributions of this paper are: (i) a complete implementation for CoAP security modes for E2E IoT security, (ii) IoT security and communication protocols for a cloud platform for the IoT, and (iii) detailed experimental evaluation and benchmarking of E2E security between a network of smart things and a cloud platform. Funding details: VINNOVA; Funding text: This research has partly been funded by the Strategic Innovation Program for Internet of Things (SIP-IoT), a joint venture of VINNOVA, Formas och Energimyndigheten; and partly by the EU H2020 project NobelGrid under grant no. 646184.CEBOT;NOBELGRIDVINNOVA;EU, Horisont 2020

Industry 4.0 has been identified as a major contributor to the era of digitalisation. Its implications for sustainable development have gained widespread attention from the perspectives of the triple bottom line, sustainable business models and circular economy. The purpose of this paper is to map the broad field of sustainable development and investigate the key research areas which comprises the aforementioned perspectives under Industry 4.0 framework. A systematic mapping review was conducted by searching five databases for relevant literature published between January 1, 2012 and April 17, 2020. The search yielded 4291 papers of which 81 were identified as primary papers relevant to the research herein. The primary findings are that the majority of sustainability research focuses on conceptual analysis, and the Internet of Things is dominantly cited with an emphasis on achieving the triple bottom line benefits. Sustainable development in the Industry 4.0 context contributes to circular economic objectives by achieving social, economic, and environmental benefits. Triple bottom line studies mainly focus on Industry 4.0 adoption and implementation, sustainable supply chains, smart and sustainable cities, and smart factories. Circular economy and sustainable business models as emerging research themes that focus on Industry 4.0 adoption and implementation, as well as sustainable supply chains. Our analysis consolidates emerging research patterns areas in both the Industry 4.0 and sustainability literature. Furthermore, it identifies salient research gaps and suggests future research.

Computer simulation models can generate large numbers of scenarios, far more than can be effectively utilized in most decision support applications. How can one best select a small number of scenarios to consider? One approach calls for choosing scenarios that illuminate vulnerabilities of proposed policies. Another calls for choosing scenarios that span a diverse range of futures. This paper joins these two approaches for the first time, proposing an optimization-based method for choosing a small number of relevant scenarios that combine both vulnerability and diversity. The paper applies the method to a real case involving climate resilient infrastructure for three African river basins (Volta, Orange and Zambezi). Introducing selection criteria in a stepwise manner helps examine how different criteria influence the choice of scenarios. The results suggest that combining vulnerability- and diversity-based criteria can provide a systematic and transparent method for scenario selection. Describes an optimization-based method for choosing a small number of scenarios.A combination of criteria related to vulnerability and diversity is used.The method is applied to a real case involving climate resilient infrastructure.

Concerns for parking are becoming imminent to best support the urban core. These persistent parking problems could be turned into new opportunities, brought by current trends in meeting the globally connected continuum. This paper reveals a work-in-progress to capitalize on private land properties for parking, in order to relieve stress on public agencies, create new sources of revenue, and enlist new entities in the intermediary market. These intermediaries, labelled as Parking Service Providers (or PSPs) play a broker role through advertising parking lots on a shared cloud platform. To streamline these business collaborations and related processes, physical parking lots are augmented with Internet connectivity allowing cloud-provided applications to congregate these lots into a larger inventory. The Internet of Things (IoT) paradigm expands the scope of cloud-based intelligent car parking services in smart cities, with novel applications that better regulate car-parking related traffic. This paper presents a work-in-progress agenda that contributes to new business solutions and state-of-the-art research impacts. We reveal a multi-layered system of PSP-business model through interdisciplinary research blocks where original results are expected to be made at each layer.

This paper presents a mechanically verified implementation of an algorithm for deciding the equivalence of Kleene algebra terms within the Coq proof assistant. The algorithm decides equivalence of two given regular expressions through an iterated process of testing the equivalence of their partial derivatives and does not require the construction of the corresponding automata. Recent theoretical and experimental research provides evidence that this method is, on average, more efficient than the classical methods based on automata. We present some performance tests, comparisons with similar approaches, and also introduce a generalization of the algorithm to decide the equivalence of terms of Kleene algebra with tests. The motivation for the work presented in this paper is that of using the libraries developed as trusted frameworks for carrying out certified program verification.

Innovation systems are sometimes referred to as complex systems, something that is intuitively understood but poorly defined. A complex system dynamically evolves in non-linear ways giving it unique properties that distinguish it from other systems. In particular, a common signature of complex systems is scale-invariant emergent properties. A scale-invariant property can be identified because it is solely described by a power law function, f(x) = kx^α, where the exponent, α, is a measure of scale-invariance. The focus of this paper is to describe and illustrate that innovation systems have properties of a complex adaptive system. In particular scale-invariant emergent properties indicative of their complex nature that can be quantified and used to inform public policy. The global research system is an example of an innovation system. Peer-reviewed publications containing knowledge are a characteristic output. Citations or references to these articles are an indirect measure of the impact the knowledge has on the research community. Peer-reviewed papers indexed in Scopus and in the Web of Science were used as data sources to produce measures of sizes and impact. These measures are used to illustrate how scale-invariant properties can be identified and quantified. It is demonstrated that the distribution of impact has a reasonable likelihood of being scale-invariant with scaling exponents that tended toward a value of less than 3.0 with the passage of time and decreasing group sizes. Scale-invariant correlations are shown between the evolution of impact and size with time and between field impact and sizes at points in time. The recursive or self-similar nature of scale-invariance suggests that any smaller innovation system within the global research system is likely to be complex with scale-invariant properties too. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down.

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment. QC 20221116

Topological and geometric segmentation methods provide powerful concepts for detailed field analysis and visualization. However, when it comes to a quantitative analysis that requires highly accurate geometric segmentation, there is a large discrepancy between the promising theory and the available computational approaches. In this paper, we compare and evaluate various segmentation methods with the aim to identify and quantify the extent of these discrepancies. Thereby, we focus on an application from quantum chemistry: the analysis of electron density fields. It is a scalar quantity that can be experimentally measured or theoretically computed. In the evaluation we consider methods originating from the domain of quantum chemistry and computational topology. We apply the methods to the charge density of a set of crystals and molecules. Therefore, we segment the volumes into atomic regions and derive and compare quantitative measures such as total charge and dipole moments from these regions. As a result, we conclude that an accurate geometry determination can be crucial for correctly segmenting and analyzing a scalar field, here demonstrated on the electron density field. Funding: Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2018-07085, 2019-05487]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-05973]

Artificial Intelligence (AI) is increasingly playing a significant role in the design and optimization of renewable energy systems. Many AI approaches and technologies are already widely deployed in the energy sector in applications such as generation forecasting, energy efficiency monitoring, energy storage, and overall design of energy systems. This paper provides a review of the applications of key AI techniques on the analysis, design, optimization, control, operation, and maintenance of Solar Tower systems, one of the most important types of Concentrating Solar Thermal (CST) systems. First, key AI techniques are briefly described and relevant examples of their application to CST systems in general are provided. Subsequently, a detailed review of how these AI techniques are being used to advance the state of the art of solar tower systems is presented. The review is structured around the different subsystems of a solar tower system.

Automated warehouses, as a form of cyber-physical systems (CPSs), require several components to work collaboratively to address the common business objectives of complex logistics systems. During the collaborative operations, a number of key performance indicators (KPI) can be monitored to understand the proficiency of the warehouse and control the operations and decisions. It is possible to drive and monitor these KPIs by looking at both the state of the warehouse components and the operations carried out by them. Therefore, it is necessary to represent this knowledge in an explicit and formally-specified data model and provide automated methods to derive the KPIs from the representation. In this paper, we implement a minimalistic data model for a subset of warehouse resources using linked data in order to monitor a few KPIs, namely sustainability, safety and performance. The applicability of the approach and the data model is illustrated through a use case. We demonstrate that it is possible to develop minimalistic data models through Open Services for Lifecycle Collaboration (OSLC) resource shapes which enables compatibility with the declarative and procedural knowledge of automated warehouse agents specified in Planning Domain Definition Language (PDDL). QC 20180530 SCOTT

Automated Guided Vehicles (AGVs) are widely used for materials transportation. Operating them in a platooned manner has the potential to improve safety, security and efficiency, control overall traffic flow and reduce resource usage. However, the published studies on platooning focus mainly on the design of technical solutions in the context of automotive domain. In this paper we focus on a largely unexplored theme of platooning in production sites transformed to the Industry 4.0, with the aim of providing safety and security assurances. We present an overall approach for a fault- and threat tolerant platooning for materials transportation in production environments. Our functional use cases include the platoon control for collision avoidance, data acquisition and processing by considering range, and connectivity with fog and cloud levels. To perform the safety and security analyses, the Hazard and Operability (HAZOP) and Threat and Operability (THROP) techniques are used. Based on the results obtained from them, the safety and security requirements are derived for the identification and prevention/mitigation of potential platooning hazards, threats and vulnerabilities. The assurance cases are constructed to show the acceptable safety and security of materials transportation using AGV platooning. We leveraged a simulation-based digital twin for performing the verification and validation as well as finetuning of the platooning strategy. Simulation data is gathered from digital twin to monitor platoon operations, identify unexpected or incorrect behaviour, evaluate the potential implications, trigger control actions to resolve them, and continuously update assurance cases. The applicability of the AGV platooning is demonstrated in the context of a quarry site.

We propose an innovative approach to model the probability of interlinkages in an interbank network with the use of Machine Learning techniques. More precisely we forecast the probability of a pair of banks entering into an interbank market borrower - lender relationship considering their financial characteristics and their past observed behavior. In this framework we examine a new method that employs machine learning in order to increase the accuracy of agnostic algorithms in reconstructing a financial network. The XGBOOST method is combined with both Maximum Entropy (MAXE) and Minimum Density (ANAN). The main contribution of this paper is that we enrich the information generally available for financial networks with variables that are available for the publication of banks financial statements (ensemble method). A set of agnostic models, i.e. models that the exposure allocation algorithm does not include prior information, are used as a benchmark to measure the additional benefit for applying machine learning in estimating prior network probabilities. By comparing the results between the agnostic algorithms and the ensemble method we see an increase in the accuracy and a decrease in the MAE of the financial networks on average. Our purpose is to depart from agnostic assumptions usually employed in interbank matrix allocation algorithms and take into account the financial features of the banks when assigning prior link probabilities. Our main finding is that machine learning algorithms outperforms the benchmark Logistic Regression model in interbank link forecasting and this is also reflected in the enhanced performance when overall network similarity measures are performed.

In this paper we present a construction for S-boxes using quasi-cyclic codes. We obtain S-boxes with good nonlinearity.

Message Queuing Telemetry Transport (MQTT) is a publish-subscribe protocol which is currently popular in Internet of Things (IoT) applications. Recently its 5.0 version has been introduced and ensuring that it is capable of providing services in a secure manner is of great importance. It must be noted that holistic security analysis should also evaluate protocol’s susceptibility to network covert channels. That is why in this paper we present a systematic overview of potential data hiding techniques that can be applied to MQTT 5.0. We are especially focusing on network covert channels that, in order to exchange secrets, exploit characteristic features of this MQTT version. Finally, we develop proof-of-concept implementations of the chosen data hiding techniques and conduct their performance evaluation in order to assess their feasibility in practical setups.

Public Key Infrastructure is the state-of-the-art credential management solution on the Internet. However, the millions of constrained devices that make of the Internet of Things currently lack a centralized, scalable system for managing keys and identities. Modern PKI is built on a set of protocols which were not designed for constrained environments, and as a result many small, battery-powered IoT devices lack the required computing resources. In this paper, we develop an automated certificate enrollment protocol light enough for highly constrained devices, which provides end-to-end security between certificate authorities (CA) and the recipient IoT devices. We also design a lightweight profile for X.509 digital certificates with CBOR encoding, called XIOT. Existing CAs can now issue traditional X.509 to IoT devices. These are converted to and from the XIOT format by edge devices on constrained networks. This procedure preserves the integrity of the original CA signature, so the edge device performing certificate conversion need not be trusted. We implement these protocols within the Contiki embedded operating system and evaluate their performance on an ARM Cortex-M3 platform. Our evaluation demonstrates reductions in energy expenditure and communication latency. The RAM and ROM required to implement these protocols are on par with the other lightweight protocols in Contiki’s network stack.

The state complexity of basic operations on regular languages considering complete deterministic finite automata (DFA) has been extensively studied in the literature. But, if incomplete DFAs are considered, transition complexity is also a significant measure. In this paper we study the incomplete (deterministic) state and transition complexity of some operations for regular and finite languages. For regular languages we give a new tight upper bound for the transition complexity of the union, which refutes the conjecture presented by Y. Gao et al. For finite languages, we correct the published state complexity of concatenation for complete DFAs and provide a tight upper bound for the case when the right operand is larger than the left one. We also present some experimental results to test the behavior of those operations on the average case, and we conjecture that for many operations and in practical applications the worst-case complexity is seldom reached.

In this paper, the introduction of temporal sub-stepping in Multi-Level Multi-Domain (MLMD) simulations of plasmas is discussed. The MLMD method addresses the multi-scale nature of space plasmas by simulating a problem at different levels of resolution. A large-domain "coarse grid" is simulated with low resolution to capture large-scale, slow processes. Smaller scale, local processes are obtained through a "refined grid" which uses higher resolution. Very high jumps in the resolution used at the different levels can be achieved thanks to the Implicit Moment Method and appropriate grid interlocking operations. Up to now, the same time step was used at all the levels. Now, with temporal sub-stepping, the different levels can also benefit from the use of different temporal resolutions. This saves further resources with respect to "traditional" simulations done using the same spatial and temporal stepping on the entire domain. It also prevents the levels from working at the limits of the stability condition of the Implicit Moment Method. The temporal sub-stepping is tested with simulations of magnetic reconnection in space. It is shown that, thanks to the reduced costs of MLMD simulations with respect to single-level simulations, it becomes possible to verify with realistic mass ratios scaling laws previously verified only for reduced mass ratios. Performance considerations are also provided. QC 20150408

Background When the Zika virus outbreak became a global health emergency in early 2016, the scientific community responded with an increased output of Zika-related research. This upsurge in research naturally made its way into academic journals along with editorials, news, and reports. However, it is not yet known how or whether these scholarly communications were distributed to the populations most affected by Zika. Methodology/Principal findings To understand how scientific outputs about Zika reached global and local audiences, we collected Tweets and Facebook posts that linked to Zika-related research in the first six months of 2016. Using a language detection algorithm, we found that up to 90% of Twitter and 76% of Facebook posts are in English. However, when none of the authors of the scholarly article are from English-speaking countries, posts on both social media are less likely to be in English. The effect is most pronounced on Facebook, where the likelihood of posting in English is between 11 and 16% lower when none of the authors are from English-speaking countries, as compared to when some or all are. Similarly, posts about papers written with a Brazilian author are 13% more likely to be in Portuguese on Facebook than when made on Twitter. Conclusions/Significance Our main conclusion is that scholarly communication on Twitter and Facebook of Zika-related research is dominated by English, despite Brazil being the epicenter of the Zika epidemic. This result suggests that scholarly findings about the Zika virus are unlikely to be distributed directly to relevant populations through these popular online mediums. Nevertheless, there are differences between platforms. Compared to Twitter, scholarly communication on Facebook is more likely to be in the language of an author’s country. The Zika outbreak provides a useful case-study for understanding how scientific outputs are communicated to relevant populations. Our results suggest that Facebook is a more effective channel than Twitter, if communication is desired to be in the native language of the affected country. Further research should explore how local media—such as governmental websites, newspapers and magazines, as well as television and radio—disseminate scholarly publication.