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Cloud computing is an emerging technology where organizations can have flexibility of their infrastructure and can store and retrieve data from cloud storage. The main advantages of cloud computing are speed, cost reduction and scalability. Cloud computing is mainly developed on virtualization of re-sources with reusability capabilities. In virtualization computer systems are deployed virtually as software and hardware resources are shared between different virtual machines. It allows multiple operating systems and software applications run on a single server. Virtualization plays an important role in the cloud environment. Though cloud computing improves the performance and quality of work for organizations it has security, privacy and trust related threads. In this paper we have surveyed the security aspects of virtualization. We have discussed the cloud computing and virtualization followed by de-tailed analysis of virtualization security.

As artificial intelligence systems permeate society, it becomes clear that aligning the behaviour of these systems with the values of those involved and affected by them is needed. The value alignment problem is widely recognised yet needs addressing in a principled way. This paper investigates how such a principled approach regarding online institutions a class of multiagent systems can provide key insights on how the value alignment problem can be addressed in general.

As artificial intelligence systems permeate society, it becomes clear that aligning the behaviour of these systems with the values of those involved and affected by them is needed. The value alignment problem is widely recognised yet needs addressing in a principled way. This paper investigates how such a principled approach regarding online institutions a class of multiagent systems can provide key insights on how the value alignment problem can be addressed in general.

This research is an interdisciplinary work between data scientists, innovation management researchers and experts from Swedish academia and a hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection with the motivation of controlling and preventing healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is called AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository. We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.

I verkliga beslutsanalyser, oavsett om det är i mänskliga övervägda situationer eller icke-mänskliga situationer (semi-intelligent maskin/agent i realtid), där är väldokumenterade problem när det gäller framkallande av sannolikheter, verktyg, och kriterievikter. I detta dokument undersöker vi automatisk viktgenerering med flera kriterier metoder med en detaljerad undersökningsmetod som inte setts tidigare. De resultaten bekräftar att Sum Rank-metoden för ordinalfallet och motsvarande Cardinal Sum Rank-metoden för kardinalfallet, överträffar alla andra metoder för robusthet. Nya fynd inkluderar att det inte finns någon indikation att skillnaden i resultat i viktgenereringen minskar eftersom antalet frihetsgrader växer vilket tidigare ansågs vara sant. Som väntat överträffar kardinalmodellerna vidare de ordinarie modellerna. Mer oväntat, men prestandan för den dominans intensitetsbaserade vikten modeller är på sin höjd mediokra för vissa kombinationer och inte ens lämplig för andra kombinationer. En annan insikt från utredningen i denna tidning är den tidigare litteraturen är inte homogen i modelleringen av attributvärdena, vilket resulterar i inte alla metoder som beaktas i denna undersökning kan direkt jämföras., In real-life decision analysis, whether in human-deliberated situations or non-human (real-time semi-intelligent machine/agent) situations, there are well-documented problems regarding the elicitation of probabilities, utilities, and criteria weights. In this paper, we investigate automatic multi-criteria weightgenerating methods with a detailed investigation method not seen before. The results confirm that the Sum Rank method for the ordinal case, and the corresponding Cardinal Sum Rank method for the cardinal case, outperform all other methods regarding robustness. New findings include that there is no indication that the difference in the results in the weight generation is diminished as the number of degrees of freedom grows which was previously thought to be true. Further, as expected the cardinal models outperform the ordinal models. More unexpectedly, though, the performance of the dominance intensity-based weight models is at most mediocre for some combinations and not even suitable for other combinations. Another insight from the investigation in this paper is that previous literature is not homogeneous in the modelling of the attribute values, resulting in not all methods considered in this investigation can be directly compared.

Collections of time series can be grouped over time both globally, over their whole time span, as well as locally, over several common time ranges, depending on the similarity patterns they share. In addition, local groupings can be persistent over time, defining associations of local groupings. In this paper, we introduce Z-Grouping, a novel framework for finding local groupings and their associations. Our solution converts time series to a set of event label channels by applying a temporal abstraction function and finds local groupings of maximized time span and time series instance members. A grouping-instance matrix structure is also exploited to detect associations of contiguous local groupings sharing common member instances. Finally, the validity of each local grouping is assessed against predefined global groupings. We demonstrate the ability of Z-Grouping to find local groupings without size constraints on time ranges on a synthetic dataset, three real-world datasets, and 128 UCR datasets, against four competitors.

What happens when scholars approach novel phenomena such as Interactive Digital Narrative (IDN)? How can we be certain that theoretical frameworks, analytical approaches, and vocabulary are adequate,which means that they are able to fully describe the specific characteristics of the novel phenomena? The same goes for approaches in the practice - how can we be sure that the chosen design methods enable the use of the full expressive potential of a novel phenomena? Furthermore, we ask how we can critique and improve categories and approaches? We reflect on how theory and analytical approaches have been produced so far, identify issues with the current practice, consider alternatives and propose a number of measures to improve the situation. Amongst them are increased efforts on the meta-level in terms of theoretical development and reflective works which concern themselves with the further development of the field, iterative approaches towards theory and method, a more critical approach in education, multi-method analysis, and dynamic representations.

Educational concepts are the core of teaching and learning. From the perspective of educational technology, concepts are essential meta-data, represen- tative terms that can connect different learning materials, and are the foundation for many downstream tasks. Some studies on automatic concept extraction have been conducted, but there are no studies looking at the K-12 level and focused on the Swedish language. In this paper, we use a state-of-the-art Swedish BERT model to build an automatic concept extractor for the Biology subject using fine- annotated digital textbook data that cover all content for K-12. The model gives a recall measure of 72% and has the potential to be used in real-world settings for use cases that require high recall. Meanwhile, we investigate how input data fea- tures influence model performance and provide guidance on how to effectively use text data to achieve the optimal results when building a named entity recognition (NER) model.

Epidemics of infectious diseases can pose a serious threat to public health and the global economy. Despite scientific advances, containment and mitigation of infectious diseases remain a challenging task. In this paper, we investigate the potential of reinforcement learning as a decision making tool for epidemic control by constructing a deep Reinforcement Learning simulator, called EpidRLearn, composed of a contact-based, age-structured extension of the SEIR compartmental model, referred to as C-SEIR. We evaluate EpidRLearn by comparing the learned policies to two deterministic policy baselines. We further assess our reward function by integrating an alternative reward into our deep RL model. The experimental evaluation indicates that deep reinforcement learning has the potential of learning useful policies under complex epidemiological models and large state spaces for the mitigation of infectious diseases, with a focus on COVID-19.

A pattern is a concept widely used in engineering and other disciplines for variety of purpose, e.g. business or system analysis. In this work, we present the concept of patterns usage within enterprise modelling. The undertaking is a part of the larger case study dedicated to Fractal Enterprise Modelling (FEM) development deploying Design Science (DS) research methodology. Based on the case study, we have identified three FEM related patterns: one modelling pattern and two analyzing patterns. A modeling pattern advises on a proper way of building a model for a particular purpose, while an analysis pattern helps to find places in a business where a decision could/should be made. More precisely, the modelling pattern identified in this study helps to build a model on an appropriate level of granularity for a certain purpose (operational decision-making). The analysis patterns identified in the study are divided into two categories of patterns: transformational patterns and problem patterns. Transformational pattern is a pattern that contains a condition and an action parts that represent a standardized solution or an opportunity. Problem pattern is a pattern that expresses conditions where a problem might exist without specifying a definite action. These patterns contribute into the creation of the bank of patterns to guide practitioners in modelling and analyzes of the business situations using FEM.

In reinforcement learning, an agent makes decisions to maximize rewards in an environment. Rewards are an integral part of the reinforcement learning as they guide the agent towards its learning objective. However, having consistent rewards can be infeasible in certain scenarios, due to either cost, the nature of the problem or other constraints. In this paper, we investigate the problem of delayed, aggregated, and anonymous rewards. We propose and analyze two strategies for conducting policy evaluation under cumulative periodic rewards, and study them by making use of simulation environments. Our findings indicate that both strategies can achieve similar sample efficiency as when we have consistent rewards.

Automated Essay Scoring (AES) automatically assigns scores to essays at scale and may help to support teachers' grading activities. Recently, AES methods based on deep neural networks (DNN) have significantly improved upon the state-of-the-art performance by learning relations between holistic essay scores and student essays. However, DNN-based AES methods function like black-box, negatively affecting the ability to provide automated writing evaluation (AWE). In this work, we proposed a new method, topic-aware BERT, based on fine-tuning the pre-trained language model to learn relations between essay scores and text representations of student essays as well as topical information in essay writing instructions. Moreover, we propose an approach to automatically retrieve key topical sentences in student essays by probing self-attention maps in intermediate layers of topic-aware BERT. We evaluate the performance of topic-aware BERT to (i) perform AES and (ii) retrieve key topical sentences using the open dataset Automated Student Assessment Prize and a manually annotated dataset, respectively. Our model achieves a strong AES performance compared with previous state-of-the-art DNN-based methods and shows effectiveness in identifying key topical sentences in argumentative essays.

This paper presents a study of retellings about Genshin Impact, exploring how the game’s narrative design inspires players to create fiction and art based on the game’s universe. A questionnaire sent to players rendered 1606 replies. Based on the findings in this corpus, eight players and creators of retellings were interviewed in-depth. Among our findings were that players were most inspired by the characters, detailed worldbuilding, and regional cultures in the game world. Their motivation to create was often spurred by the gaps and ambiguities in the detailed narrative design, wanting to “fill in the gaps” and, through their creation of fiction, further explore the intricacies of the game’s narrative elements.

5G is an emerging technology. 5G is not going to be an update to its predecessors. Researchers are intended to achieve a head-turning advancement in terms of all the way performances such as, data rates, network reliability, massive connectivity, mobility, energy efficiency, latency, secured channel, spectral efficiency etc. 5G is going to be an end-to-end system that will provide hyper-connectivity to its users. It is supposed to support roughly three use cases i.e. Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC). All of these create convergence among wireless communication and computer networking that incorporates Software Driven Network (SDN), Network Functions Virtualization (NFV), Service Based Architecture (SBA), 5G new radio technologies (M-MIMO, mmWave, UDN, FD), massive IoT techniques. This hyper-convergence will introduce new trust and security threats and relevant threat management challenges. In this paper we tried to summarize on why and how 5G is evolved and what are the technology requirements for 5G revolution and steps adopted for technology change over to 5G. We focused on potential threats and challenges and their suggested mitigation techniques. A number of open issues are identified and probable future research directions in the very field are also discussed in this paper.

Today, collaborative learning has become quite central as a method for learning, and over the past decades, a large number of studies have demonstrated the benefits from various theoretical and methodological perspectives. This study proposes a novel approach that utilises Natural Language Processing(NLP) methods, particularly pre-trained word embeddings, to automatically create homogeneous or heterogeneous groups of students in terms of knowledge and knowledge gaps expressed in assessments. The two different ways of creating groups serve two different pedagogical purposes: (1) homogeneous group formation based on students’ knowledge can support and make teachers’ pedagogical activities such as feedback provision more time efficient, and (2) the heterogeneous groups can support and enhance collaborative learning. We evaluate the performance of the proposed approach through experiments with a dataset from a university course in programming didactics.

Value creation and value capture are the two main concepts within strategic management. This because of firm’s revenue depends on how these processes are managed. Value creation are associated with value chain processes that generate costs in a balance sheet. Thus, these are mostly tangible. Whereas, the value capture processes are associated with the processes intended to create any type of competitive advantage. Such advantage might be hidden in firm-specific routines or resources. Thus, value capture processes are ambiguous and often intangible. Practically, it is a challenging task to identify and manage them. This paper discusses how Enterprise Modelling (EM) may assist in detecting of this type of processes, particularly, Fractal Enterprise Model (FEM) is used for the challenge. A hypothetical example for detecting value capture processes is given using FEM technique.

Process mining is an area of research that supports discovering information about business processes from their execution event logs. One of the challenges in process mining is to deal with the increasing amount of event logs and the interconnected nature of events in organizations. This issue limits the organizations to apply process mining on a large scale. Therefore, this paper introduces and formalizes a new approach to store and retrieve event logs into/from graph databases. It defines an algorithm to compute Directly Follows Graph (DFG) inside the graph database, which shifts the heavy computation parts of process mining into the graph database. Calculating DFG in graph databases enables leveraging the graph databases' horizontal and vertical scaling capabilities to apply process mining on a large scale. We implemented this approach in Neo4j and evaluated its performance compared with some current techniques using a real log file. The result shows the possibility of using a graph database for doing process mining in organizations, and it shows the pros and cons of using this approach in practice.

The EMMSAD (Exploring Modeling Methods for Systems Analysis and Development) conference series organized 24 events from 1996 to 2019, associated with CAiSE (Conference on Advanced Information Systems Engineering). From 2009, EMMSAD has become a two-days working conference. From 2017, EMMSAD best papers are invited to submit extended versions for considering their publication in the Journal of Software and Systems Modeling (SoSyM). The main topics of the EMMSAD series have the focus on models and modeling methods for software and information systems development, requirements engineering, enterprise modeling and architecture, and business process management. The conference further addresses evaluation of modeling methods through a variety of empirical and nonempirical approaches.

As social context becomes a more central concept for social simulation,different approaches to context have been developed. We discuss four of these with the help of an overall Contextual Action Framework for Computational Agents (CAFCA). More in particular, we describe how the consumat model, social norms,collective reasoning, and social practices can be related to each other using CAFCA. Following this we show how these approaches than can co-exist in the analysis and simulation of social phenomena rather than compete or be seen as mutually exclusive.

The development of intelligent automobiles puts forward higher requirements for HMI design. The information obtained by drivers through vision accounts for 80%. As a key part of visual perception, color affects driving performance. This article investigates vehicle interface design about icon-background contrast. Three colors commonly used in car-machine interface are chosen: red, green and blue, and contrast is divided into five levels under daytime and nighttime. The participants tested three colors and the five contrasts in an experiment using classic paradigm of secondary tasks. We collected data from their driving behavior and subjective experience, trying to find a most appropriate value. The results show that recommended contrast values under different lighting conditions are 3:1 to 4:1 for red, blue, and green during the day; 3:1 for red at night, and 5:1 for blue and green, so that the vehicle central console color design references are formed.

The Internet of Things (IoT) paradigm consists of smart sensor objects and their related applications. All these intelligent sensors communicate and transfer data via the internet. Though communication between IoT devices and other network components is mostly performed over traditional Internet infrastructure, this process is still much more complex and different from a conventional client-server communication process. In general, both parties are authenticated and authorized via a centralized identity and authorization management server in a traditional client-server or peer-to-peer communication network architecture. But in an IoT infrastructure, the IoT devices, gateways, end-user applications need to be authenticated dynamically during or before the communication processes begin. Another issue in the communication process is secure communication between components of the IoT paradigm. Due to the heterogeneous behavior and dynamic communication between IoT devices, it is hard to define a standard way to secure communication between IoT infrastructure and cloud computing components. A new seven layers of IoT communication architecture is proposed to address the above-mentioned issues, where data communication and processing are done in a decentralized and distributed manner.

Intelligent Transportation Systems employ different localization technologies, such as the Global Navigation Satellite System. This system transmits signals between satellite and receiver devices on the ground which can estimate their position on earth’s surface. The accuracy of this positioning estimate, or the positioning error estimation, is of utmost importance for the efficient and safe operation of autonomous vehicles, which require not only the position estimate, but also an estimation of their operation margin. This paper proposes a workflow for positioning error estimation using a random forest regressor along with a post-hoc conformal prediction framework. The latter is calibrated on the random forest out-of-bag samples to transform the obtained positioning error estimates into predicted integrity intervals, which are confidence intervals on the positioning error prediction with at least 99.999% confidence. The performance is measured as the number of ground truth positioning errors inside the predicted integrity intervals. An extensive experimental evaluation is performed on real-world and synthetic data in terms of root mean square error between predicted and ground truth positioning errors. Our solution results in an improvement of 73% compared to earlier research, while providing prediction statistical guarantees.

In response to the COVID-19 pandemic, governments around the world are taking a wide range of measures. Previous research on COVID-19 has focused on disease spreading, epidemic curves, measures to contain it, confirmed cases, and deaths. In this work, we sought to explore another essential aspect of this pandemic, how do people feel and react to this reality and the impact on their emotional well-being. For that reason, we propose using epidemic indicators and government policy responses to estimate the sentiment, as this is expressed on Twitter. We develop a nowcasting approach that exploits the time series of epidemic indicators and the measures taken in response to the COVID-19 outbreak in the United States of America to predict the public sentiment at a daily frequency. Using machine learning models, we improve the short-term forecasting accuracy of autoregressive models, revealing the value of incorporating the additional data in the predictive models. We then provide explanations to the indicators and measures that drive the predictions for specific dates. Our work provides evidence that data about the way COVID-19 evolves along with the measures taken in response to the COVID-19 outbreak can be used effectively to improve sentiment nowcasting and gain insights into people’s current emotional state.

The advent of cloud computing and autonomous data cen- ters operating fully without human supervision has highlighted the need for fault-tolerant architectures and intelligent software tools for system parameter optimization. Demands on computational throughput have to be balanced with environmental concerns, such as energy consump- tion and waste heat. Using multivariate time series data collected from an experimental data center, we build a state model using clustering, then estimate the states represented by the clusters using both a hidden Markov model and a long-short term memory neural net. Knowledge of future states of the system can be used to solve tasks such as reduced energy consumption and optimized resource allocation in the data center.

As human population grows in number, the amount of (organic and non-organic) waste materials has grown rapidly year by year. Changes of consumption and lifestyle have generated a higher waste amount. Waste management has become a significant issue in today’s society. In 2014, the EU countries registered 2.494 million tons of generated waste, which was an increase in growth of 2.8% compared with data from 2008. Different renewable materials are ending up as waste, such as glass, paper, plastic, textile, which may be used in a recycling process. This chapter will discuss these challenges with the focus on one of these materials, textiles, as building materials. We also introduce the perspective of Human-Computer Interaction (HCI) aspects, and especially Human-Built Environment Interaction which will give some specific focus on textiles used as recycled materials. The result of the critical literature review in the area of textiles as building material from an HCI point of view suggest a set of interaction design dimensions that can be considered and applied on the usage of textiles for built environments.

This book constitutes the proceedings papers of the 14th IFIP Working Conference on the Practice of Enterprise Modeling, held in Riga, Latvia, during November 24-26, 2021. PoEM offers a forum for sharing experiences and knowledge between the academic community and practitioners from industry and the public sector. This year the theme of the conference is the use of enterprise modeling and enterprise architecture towards ensuring sustainability and resilience of enterprises and societies. The 14 full and 6 short papers presented in this volume were carefully reviewed and selected from a total of 47 submissions. They were organized in topical sections named: enterprise modeling and enterprise architecture; enterprise modeling methods and method engineering; business process modeling and management; requirements engineering for privacy, security and governance; and case studies and experiences.

Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this paper, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. More concretely, each event interval sequence is represented by a bipartite graph by following three main steps: (1) creating a hash table that can quickly convert a collection of event interval sequences into a bipartite graph representation, (2) creating and regularizing a bi-adjacency matrix corresponding to the bipartite graph, (3) defining a spectral embedding mapping on the bi-adjacency matrix. In addition, we show that substantial improvements can be achieved with regard to classification performance through pruning parameters that capture the nature of the relations formed by the event intervals. We demonstrate through extensive experimental evaluation on five real-world datasets that our approach can obtain runtime speedups of up to two orders of magnitude compared to other state-of-the-art methods and similar or better clustering and classification performance.

Multivariate histogram snapshots are complex data structures that frequently occur in predictive maintenance. Histogram snapshots store large amounts of data in devices with small memory capacity, though it remains a challenge to analyze them effectively. In this paper, we propose Z-Hist, a novel framework for representing and temporally abstracting histogram snapshots by converting them into a set of temporal intervals. This conversion enables the exploitation of frequent arrangement mining techniques for extracting disproportionally frequent patterns of such complex structures. Our experiments on a turbo failure dataset from a truck Original Equipment Manufacturer (OEM) demonstrate a promising use-case of Z-Hist. We also benchmark Z-Hist on six synthetic datasets for studying the relationship between distribution changes over time and disproportionality values.