Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the duration for an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to delayed emergency response times, potentially impacting patient outcomes. In the current paper, we propose a novel framework for accurately estimating ambulance travel times using machine learning paradigms, employing real-world spatiotemporal ambulance data from the Skane region, Sweden. Our framework includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. First, through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. Then, we present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trip scenarios and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy. Our experiments indicate that the aforementioned factors play a significant role when estimating the travel time.

The development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms.

The benefit associated with the deployment of Internet of Things (IoT) technology is increasing daily. IoT has revolutionized our ways of life, especially when we consider its applications in smart connected homes. Smart devices at home enable the collection of data from multiple sensors for a range of applications and services. Nevertheless, the security and privacy issues associated with aggregating multiple sensors’ data in smart connected homes have not yet been sufficiently prioritized. Along this development, this paper proposes HOMEFUS, a privacy and security-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors’ data at the edge nodes of smart connected homes. HOMEFUS employs federated learning, edge and cloud computing to reduce privacy leakage of sensitive data. To demonstrate its applicability, we show that the proposed model meets the requirements for efficient data fusion pipelines. The model guides practitio ners and researchers on how to setup secure smart connected homes that comply with privacy laws, regulations, and standards. 

Internet of Things (IoT) technology has created a new dimension for data collection, transmission, processing, storage, and service delivery. With the advantages offered by IoT technologies, interest in smart home automation has increased over the years. Nevertheless, smart connected homes are characterized with the security and privacy problems that are associated with aggregating multiple sensors' data and exposing them to the Internet. In this paper, we propose LPM, a lightweight privacy-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors' data at the edge nodes of smart connected homes. LPM employs federated learning, edge and cloud computing to reduce privacy leakages of sensitive data. To demonstrate its applicability, two services, commonly provided by smart homes, i.e., occupancy detection and people count estimation, were experimentally investigated. The results show that LPM can achieve accuracy, F1 score and AUC-ROC of 99.98%, 99.13%, and 99.98% respectively for occupancy detection as well as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 of 0.0011,0.0175, and 98.39% respectively for people count estimation. LPM offers the opportunity to each smart connected home to participate in collaborative learning that is achieved through the federated machine learning component of the proposed model.

Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms.

This study is concerned with digital two-dimensional rivers for games. Specifically the aim is to create a river shape from the basis of a spline curve and make it look like a river with a water flow-rate. This is achieved by developing an artifact capable of river simulations in 2D, with the flow-rate calculated using the necessary hydraulics equations. The scientific process described in Design Science Research Methodology for Information Systems Research is presented and followed. Artifact simulations are demonstrated and evaluated, especially calculations for river mean velocity and discharge are shown to be possible by assuming that the river channel is of trapezoidal shape. Simulations show a type of river not usually seen in games, a river with more accuracy than other game simulations. The artifact river simulations are however limited by a missing smoothness when going between different channel shapes. Also there could be a way of improving the texture used, or possibly use more then one texture in one simulation. The conclusion is that the methods used for river generation by this study could be part of a design tool that targets 2D games.

In their seminal work on Multidimensional Sorting, Goodman and Pollack introduced the so-called order type, which for each ordered triple of a point set in the plane gives its orientation, clockwise or counterclockwise. This information is sufficient to solve many problems from discrete geometry where properties of point sets do not depend on the exact coordinates of the points but only on their relative positions. Goodman and Pollack showed that an efficient way to store an order type in a matrix λ of quadratic size (w.r.t. the number of points) is to count for every oriented line spanned by two points of the set how many of the remaining points lie to the left of this line. We generalize the concept of order types to bicolored point sets (every point has one of two colors). The bicolored order type contains the orientation of each bicolored triple of points, while no information is stored for monochromatic triples. Similar to the uncolored case, we store the number of blue points that are to the left of an oriented line spanned by two red points or by one red and one blue point in λ_B. Analogously the number of red points is stored in λ_R. As a main result, we show that the equivalence of the information contained in the orientation of all bicolored point triples and the two matrices λ_B and λ_R also holds in the colored case. This is remarkable, as in general the bicolored order type does not even contain sufficient information to determine all extreme points (points on the boundary of the convex hull of the point set).We then show that the information of a bicolored order type is sufficient to determine whether the two color classes can be linearly separated and how one color class can be sorted around a point of the other color class. Moreover, knowing the bicolored order type of a point set suffices to find bicolored plane perfect matchings or to compute the number of crossings of the complete bipartite graph drawn on a bicolored point set in quadratic time.

Let P be a set of n = 2m + 1 points in the plane in general position. We define the graph GM_P whose vertex set is the set of all plane matchings on P with exactly m edges. Two vertices in GM_P are connected if the two corresponding matchings have m − 1 edges in common. In this work we show that GM_P is connected.

Let P be a set of n = 2m + 1 points in the plane in general position. We define the graph GM_P whose vertex set is the set of all plane matchings on P with exactly m edges. Two vertices in GM_P are connected if the two corresponding matchings have m − 1 edges in common. In this work we show that GM_P is connected.

Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source–destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

The time to treatment plays a major factor in recovery for stroke patients, and simulation techniques can be valuable tools for testing healthcare policies and improving the situation for stroke patients. However, simulation requires individual-level data about stroke patients which cannot be acquired due to patient’s privacy rules. This thesis presents a hybrid simulation model for generating a synthetic population of stroke patients by combining Agent-based and microsimulation modeling. Subsequently, Agent-based simulation is used to estimate the locations where strokes happen. The simulation model is built by conducting the Design Science research method, where the simulation model is built by following a set of steps including data preparation, conceptual model formulation, implementation, and finally running the simulation model. The generated synthetic population size is based on the number of stroke events in a year from a Poisson Point Process and consists of stroke patients along with essential attributes such as age, stroke status, home location, and current location. The simulation output shows that nearly all patients had their stroke while being home, where the traveling factor is insignificant to the stroke locations based on the travel survey data used in this thesis and the assumption that all patients return home at midnight.

Database forensic investigation (DBFI) is an important area of research within digital forensics. It & x2019;s importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

We study the problem of determining the complexity of the lower envelope of a collection of n geometric objects. For collections of rays; unit length line segments; and collections of unit squares to which we apply at most two transformations from translation, rotation, and scaling, we prove a complexity of Θ(n). If all three transformations are applied to unit squares, then we show the complexity becomes Θ(nα(n)), where α(n) is the slowly growing inverse of Ackermann’s function.

The threats posed by botnets in the cyber-space continues to grow each day and it has become very hard to detect or infiltrate bots given that the botnet developers each day keep changing the propagation and attack techniques. Currently, most of these attacks have been centered on stealing computing energy, theft of personal information and Distributed Denial of Service (DDoS attacks). In this paper, the authors propose a novel technique that uses the Non-Deterministic Polynomial-Time Hardness (NP-Hard Problem) based on the Traveling Salesperson Person (TSP) that depicts that a given bot, b(j), is able to visit each host on a network environment, NE, and then it returns to the botmaster in form of instruction(command) through optimal minimization of the hosts that are or may be attacked. Given that b(j) represents a piece of malicious code and based on TSP-NP Hard Problem which forms part of combinatorial optimization, the authors present an effective approach for the detection of the botnet. It is worth noting that the concentration of this study is basically on the centralized botnet architecture. This holistic approach shows that botnet detection accuracy can be increased with a degree of certainty and potentially decrease the chances of false positives. Nevertheless, a discussion on the possible applicability and implementation has also been given in this paper.

Regression analysis has been used in previous research to predict the number of bicycles registered by a bicycle counter. An important step to improve the prediction is to include a long-term trend curve estimate as part of the formulation of the regression target variable. In this way, it is possible to use the deviation from the trend curve estimate instead of the absolute number of bicycles as target variable in the regression problem formulation. This can help capturing the factors that are difficult, or even impossible, to model as input variables in the regression model, for example, larger infrastructural changes. This study aims to evaluate a regression spline-based approach to enhance the prediction accuracy of bicycle counter data. This will be achieved by formulating a regression problem, generating trend curve estimates using regression splines, and evaluating the resulted curves using cross validation on a set of chosen regression algorithms. We illustrate our approach by applying it on a time series recorded by a bicycle counter in Malmö city, Sweden. For the considered data set, our experimental results show that the spline trend curve estimate with knots between 12-19, which has been fitted to the time series, gives the best prediction. It also shows that the use of ensemble methods leads to better prediction, where the G.B. Regressor shows best performance with 19 knots.

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.

Narrative structures define the skeleton of narratives and help at identifying common structures in stories, that then can be used to compare structures, define variations, and understand prototypical [structural] components. However, narrative structures are just one piece of the puzzle, their interpretation is what gives room to the stories seen in transmedia storytelling. In principle, a structure can be interpreted and developed with a myriad of stories, but requires some type of corpus to develop it further. Large language models such as ChatGPT could be employed for this task, if we are able to define a good narrative structure and give the tools to the algorithm to develop them further. For this paper, we use a narrative structure system called TropeTwist, which employs interconnected tropes as narrative structures, defining characters, conflicts, and plot devices in a relational graph, which gives raise to a set of trope micro- and meso-patterns. Using ChatGPT and through the web interface, we communicate all the possible elements to be used from TropeTwist and tasked ChatGPT to interpret them and generate stories. We describe our process and methodology to reach these interpretations, and present some of the generated stories based on a constructed narrative structure. Our results show the possibilities and limitations of using these systems and elaborate on future work to combine large language models for other tasks within narrative interpretation and generation.

Game design is a hard and multi-faceted task that intertwines different gameplay mechanics, audio, level, graphic, and narrative facets. Games' facets are developed in conjunction with others with a common goal that makes games coherent and interesting. These combinations result in plenty of games in diverse genres, which usually require a collaboration of a diverse group of designers. Collaborators can take different roles and support each other with their strengths resulting in games with unique characteristics. The multi-faceted nature of games and their collaborative properties and requirements make it an exciting task to use Artificial Intelligence (AI). The generation of these facets together requires a holistic approach, which is one of the most challenging tasks within computational creativity. Given the collaborative aspect of games, this thesis approaches their generation through Human-AI collaboration, specifically using a mixed-initiative co-creative (MI-CC) paradigm. This paradigm creates an interactive and collaborative scenario that leverages AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges, such as Human and AI goal alignment or competing properties.

In this thesis, game design and the generation of game facets by themselves and intertwined are explored through Human-AI collaboration. The AI takes a colleague's role with the designer, arising multiple dynamics, challenges, and opportunities. The main hypothesis is that AI can be incorporated into systems as a collaborator, enhancing design tools, fostering human creativity, and reducing workload. The challenges and opportunities that arise from this are explored, discussed, and approached throughout the thesis. As a result, multiple approaches and methods such as quality-diversity algorithms and designer modeling are proposed to generate game facets in tandem with humans, create a better workflow, enhance the interaction, and establish adaptive experiences.

As AI develops, grows, and expands, the more benefits we can have from it. AI is used in multiple fields to assist humans, such as object recognition, self-driving cars, or design tools. However, AI could be used for more than assisting humans in their tasks. It could be employed to collaborate with humans as colleagues in shared tasks, which is usually described as Mixed-Initiative (MI) paradigm. This paradigm creates an interactive scenario that leverage on AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges. For instance, there must be an understanding between humans and AI, where autonomy and initiative become negotiation tokens. In addition, control and expressiveness need to be taken into account to reach some goals. Moreover, although this paradigm has a broader application, it is especially interesting for creative tasks such as games, which are mainly created in collaboration. Creating games and their content is a hard and complex task, since games are content-intensive, multi-faceted, and interacted by external users. 

Therefore, this thesis explores MI collaboration between human game designers and AI for the co-creation of games, where the AI's role is that of a colleague with the designer. The main hypothesis is that AI can be incorporated in systems as a collaborator, enhancing design tools, fostering human creativity, reducing their workload, and creating adaptive experiences. Furthermore, This collaboration arises several dynamic properties such as control, expressiveness, and initiative, which are all central to this thesis. Quality-Diversity algorithms combined with control mechanisms and interactions for the designer are proposed to investigate this collaboration and properties. Designer and Player modeling is also explored, and several approaches are proposed to create a better workflow, establish adaptive experiences, and enhance the interaction. Through this, it is demonstrated the potential and benefits of these algorithms and models in the MI paradigm.

We propose the Interactive Constrained MAP-Elites, a quality-diversity solution for game content generation, implemented as a new feature of the Evolutionary Dungeon Designer (EDD): a mixed-initiative co-creativity tool for designing dungeons. The feature uses the MAP-Elites algorithm, an illumination algorithm that segregates the population among several cells depending on their scores with respect to different behavioral dimensions. Users can flexibly and dynamically alternate between these dimensions anytime, thus guiding the evolutionary process in an intuitive way, and then incorporate suggestions produced by the algorithm in their room designs. At the same time, any modifications performed by the human user will feed back into MAP-Elites, closing a circular workflow of constant mutual inspiration. This paper presents the algorithm followed by an in-depth evaluation of the expressive range of all possible dimension combinations in several scenarios, and discusses their influence in the fitness landscape and in the overall performance of the procedural content generation in EDD.

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user’s design style to better assess the tool’s procedurally generated content with respect to that user’s preferences. Through this approach, we aim for increasing the user’s agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games’ narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.  

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games' narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.

MAP-Elites has been successfully applied to the generation of game content and robot behaviors. However, its behavior and performance when interacted with in co-creative systems is underexplored. This paper analyzes the implications of synthetic interaction for the stability and adaptability of MAP-Elites in such scenarios. We use pre-recorded human-made level design sessions with the Interactive Constrained MAP-Elites (IC MAP-Elites). To analyze the effect of each edition step in the search space over time using different feature dimensions, we introduce Temporal Expressive Range Analysis (TERA). With TERAs, MAP-Elites is assessed in terms of its adaptability and stability to generate diverse and high-performing individuals. Our results show that interactivity, in the form of design edits and MAP-Elites adapting towards them, directs the search process to previously unexplored areas of the fitness landscape and points towards how this could improve and enrich the co-creative process with quality-diverse individuals.

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system’s adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative.  

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms, i.e., Designer Personas.

Quests are a core element in many games, especially role-playing and adventure games, where quests drive the gameplay and story, engage the player in the game’s narrative, and in most cases, act as a bridge between different game elements. The automatic generation of quests and objectives is an interesting challenge since this can extend the lifetime of games such as in Skyrim, or can help create unique experiences such as in AI Dungeon. This work presents Questgram [Qg], a mixed-initiative prototype tool for creating quests using grammars combined in a mixed-initiative level design tool. We evaluated our tool quantitatively by assessing the generated quests and qualitatively through a small user study. Human designers evaluated the system by creating quests manually, automatically, and through mixed-initiative. Our results show the Questgram’s potential, which creates diverse, valid, and interesting quests using quest patterns. Likewise, it helps engage designers in the quest design process, fosters their creativity by inspiring them, and enhance the level generation facet of the Evolutionary Dungeon Designer with steps towards intertwining both level and quest design.

A mobile stroke unit (MSU) is a specialized ambulance, enabling to shorten the time to diagnosis and treatment for stroke patients. In the current paper, we present a simulation-based approach to study the potential impacts of collaborative use of regular ambulances and MSUs in prehospital transportation for stroke patients, denoted as co-dispatching. We integrated a co-dispatch policy in an existing modeling framework for constructing emergency medical services simulation models. In a case study, we applied the extended framework to southern Sweden to evaluate the effectiveness of using the co-dispatch policy for different types of stroke. The results indicate reduced time to diagnosis and treatment for stroke patients when using the co-dispatch policy compared to the situation where either a regular ambulance or an MSU is assigned for a stroke incident.

In this paper an experimental system assisted by emerging digital technologies is developed, for monitoring and controlling invasive water hyacinth weed and rescue operation of freshwater lakes in Africa. The system is designed to integrate fifth generation ultra-reliable low latency communication (5G URLLC), unmanned aerial vehicles (UAV), underwater robots, smart environmental sensing with internet of things (IoT) and machine learning techniques for real time monitoring, managing, controlling and predicting the expansion of invasive water hyacinth weed. The experimental system for sensor data collection implemented on lake Tana in Ethiopia will be expanded to other fresh water lakes of Africa affected by water hyacinth weed. System modeling and data analytics based on sensor data will be performed to generate decision inference for controlling the growth of water hyacinth in the water bodies of the lake. Environmental data collection from other local sources will be integrated with sensor data for further system modeling and critical action analysis and implementation using machine learning algorithms to remove the main causes for the rapid expansion of water hyacinth throughout the lake.

Fuzzing is a test method used to automatically generate test case inputs and to executea system under test (SUT) with those inputs. The method is traditionally used to discovercrash-inducing bugs in software. Fuzzing can generate thousands of inputs per secondand many implementations use smart techniques to reach deeply into the code. Fewfuzz testing implementations, however, have the ability to explore and retain informationof state in stateful applications.

We develop an extension of the fuzzer American Fuzzy Lop (AFL), building on the workof the Ijon project, and utilize its fuzzing capabilities to discover states in SUT; inparticular, applications built as finite state machines. The extension successfullyharnesses AFL’s input generation to explore the SUT’s state space.

We then implement functionality that allows for the SUT to return state information tothe fuzzer, including the state path and path length. Furthermore, functionality is addedthat allows the test operator to specify the expected number of states in the SUT, andGUI extensions that provide real-time information of state discovery during fuzzing.

The state information retained after a completed fuzzing session is automaticallysummarized in a structured format. We further demonstrate that the summarizedinformation can be used to generate test cases for a test operator to verify the SUT.

Elements of randomness are a very common factor in modern digital games, from simple rolls of a die to complex AI systems. These elements have an impact on how the player experiences a game. We believe that exploring the field of luck analysis can benefit designers through a developed understanding of how such elements affect players. In this study, we explore how elements of randomness affect players in the roguelike deckbuilding game, Slay the Spire using data clustering. Three player skill groups were identified with the use of clustering: Winners, Low skill losers and High skill losers. Our results indicate that people who succeeded in beating the game, had an increased amount of randomness in the form of cards by a factor of 1.82. Showing that more skilled players do not shy away from randomness but instead embrace it more than lower skilled players.

The adoption of artificial intelligence (AI) techniques entails a substantial change in the wireless ecosystem where data as well as their owners become crucial. As a result, the roll out of AI techniques in wireless systems raises a plethora of questions. In this context, we describe the challenges observed by the wireless stakeholders when deploying AI. Furthermore, we introduce the recent discussion in field of ethics that appear when managing wireless communications data.

Online routing in a planar embedded graph is central to a number of fields and has been studied extensively in the literature. For most planar graphs no O (1)-competitive online routing algorithm exists. A notable exception is the Delaunay triangulation for which Bose and Morin (SIAM J Comput 33(4):937-951, 2004) showed that there exists an online routing algorithm that is O(1)-competitive. However, a Delaunay triangulation can have Omega (n) vertex degree and a total weight that is a linear factor greater than the weight of a minimum spanning tree. We show a simple construction, given a set V of n points in the Euclidean plane, of a planar geometric graph on V that has small weight (within a constant factor of the weight of a minimum spanning tree on V), constant degree, and that admits a local routing strategy that is O (1)-competitive. Moreover, the technique used to bound the weight works generally for any planar geometric graph whilst preserving the admission of an O (1)-competitive routing strategy.

Däckbranschens verksamhet är till stor del säsongsberoende och kan vid specifika tidpunkter stå inför utmaningar med att en större mängd kunder vill byta däck samtidigt. Detta innebär i många fall långa köer och i sin tur påfrestningar på verksamheten. I denna rapport har ett självbetjäningssystem i form av en mobil webbapplikation utvecklats med avsikten att undersöka hur denna uppfattas av tänkta slutanvändare. Den utvecklade applikationen ersätter ett vanligt kölappssystem genom att en bilverkstads kunder ska kunna stå i kö med en digital nummerlapp som presenteras i användarens mobil. Detta för att hantera flödet av verkstädernas kunder. I studien har användartester genomförts där nio tänkta slutanvändare har provat applikationen. Dessa har i samband med användartesterna intervjuats med semi-strukturerade intervjuer för att undersöka dess apersoners uppfattning av applikationen. Technology Acceptance Model har använts för att ta fram frågor som fokuserar på hur användaren uppfattar applikationen gällande applikationens nytta och hur lätthanterlig de uppfattar den. Analysen visar att en majoritetuppfattar applikationen som enkel att hantera och gynnsam då ett däckbyte skagenomföras. Anledningar till detta som framkommer av genomförda intervjuer är bland annat avlastning för personalen på bilverkstäder, mindre kundkontakt och bekvämarevistelse för verkstädernas kunder. 

In the current thesis, Alphabet Inc and Facebook Inc, which are two of the five major tech companies included in the digital ecosystem, will be analyzed. Alphabet Inc and Facebook Inc have been selected because their business model is based on using private data as a commodity. These companies will be analyzed based on the privacy paradox that arises when users have knowledge of their privacy while still agreeing to the terms of service without reading those that conflict with their privacy.  This study has used a survey as a basis to support literature and the authors hypothesis to be able to answer the given research question. The results show that these companies use language that is difficult to understand, long and complicated terms of service, which results in the user not reading through them. At the same time, there is an awareness of the privacy paradox, which creates a paradox when the user does not read the user agreements. The conclusions that come with the work are the factors that lead to the privacy paradox and its effects. At the same time as implications regarding consequences on society.    

We present an O(n² · min{m, n}) time and O(n · min{m, n}) storage algorithm to compute the minsum set of m watchmen routes given their starting points in a Stiegl polygon ― a staircase polygon where the floor solely consists of one horizontal and one vertical edge ― with n vertices.

Although smart homes are tasked with an increasing number of everyday activities to keep users safe, healthy, and entertained, privacy concerns arise due to the large amount of personal data in flux. Privacy is widely acknowledged to be contextually dependent, however, the interrelated stakeholders involved in developing and delivering smart home services – IoT developers, companies, users, and lawmakers, to name a few – might approach the smart home context differently. This paper considers smart homes as digital ecosystems to support a contextual analysis of smart home privacy. A conceptual model and an ecosystem ontology are proposed through design science research methodology to systematize the analyses. Four privacy-oriented scenarios of surveillance in smart homes are discussed to demonstrate the utility of the digital ecosystem approach. The concerns pertain to power dynamics among users such as main users, smart home bystanders, parent-child dynamics, and intimate partner relationships and the responsibility of both companies and public organizations to ensure privacy and the ethical use of IoT devices over time. Continuous evaluation of the approach is encouraged to support the complex challenge of ensuring user privacy in smart homes.

We study extremal problems about faces in convex rectilinear drawings of K_n, that is, drawings where vertices are represented by points in the plane in convex position and edges by line segments between the points representing the end-vertices. We show that if a convex rectilinear drawing of K_n does not contain a common interior point of at least three edges, then there is always a face forming a convex 5-gon while there are such drawings without any face forming a convex k-gon with k ≥ 6.

A convex rectilinear drawing of K_n is regular if its vertices correspond to vertices of a regular convex n-gon. We characterize positive integers n for which regular drawings of K_n contain a face forming a convex 5-gon.

To our knowledge, this type of problems has not been considered in the literature before and so we also pose several new natural open problems.

The multiple functionalities of mobile devices have allowed them to be used for contact-tracing especially with the emergence of an infectious pandemic, for example, in a smart city. This has been experienced, for example, in COVID-19 cases where propagation of infections may not be controlled effectively. Given that data is exchanged between parties it becomes important to have a focus on how this data can be used as a contact trace mechanism. This contract trace mechanism can also provide Potential Digital Evidence (PDE) that can aid to form an objective hypothesis that could be employed during litigation in the event of a suspicious infection, or when a security incident is detected. This paper, therefore, proposes an iterative Concurrent Contact-Tracing (CCT) framework based on digital evidence from mobile devices in heterogeneous environments. 

The 18th IEEE International Conference on Software Architecture (ICSA 2021) solicited different types of submissions structured into the following tracks: The main Technical Track (included in the ICSA main proceedings), the Software Architecture in Practice (SAIP) track, the New and Emerging Ideas (NEMI) track, the Early Career Researchers Forum (ECRF), the Journal First track, and the Workshop track (included in the companion volume of ICSA 2021 proceedings). Each of these tracks, except for the Technical Track, is presented in the following sections. © 2021 IEEE.

Agent-based models have long been argued a useful toolto support policy analysis, variably targeting the assessment of policydesign, as well as establishing its performance. Challenging, however,remains appropriate empirical parameterization and validation of suchmodels. This paper contributes to the development of rigorous accountsof policy modelling primarily driven by policy documents in order to develop general conceptual model. Such models can then serve as a basis forearly validation by subject matter experts, but more importantly, informthe subsequent inquiry relevant for the parameterization of such models, while at the same time offering the opportunity to detect deviationsfrom regulated practice. Relying on the scenario of organ donation basedon the Swedish legislation, we explore the merits of such an approach,and sketch the individual steps from policy documents to conceptualmodel. Supporting the methodological process, this paper employs theInstitutional Grammar 2.0, which offers selected features supporting theproposed modelling approach.

Deep neural networks for positioning can improve accuracy by adapting to inhomogeneous environments. However, they are still susceptible to noisy data, often resulting in invalid positions. A related task, map matching, can be used for reducing geographical invalid positions by aligning observations to a model of the real world. In this paper, we propose an approach for positioning, enhanced with map matching, within a single deep neural network model. We introduce a novel way of reducing the number of invalid position estimates by adding map information to the input of the model and using a map-based loss function. Evaluating on real-world Received Signal Strength Indicator data from an asset tracking application, we show that our approach gives both increased position accuracy and a decrease of one order of magnitude in the number of invalid positions.

Deep neural networks have the ability to generalize beyond observed training data. However, for some applications they may produce output that apriori is known to be invalid. If prior knowledge of valid output regions is available, one way of imposing constraints on deep neural networks is by introducing these priors in a loss function. In this paper, we introduce a novel way of constraining neural network output by using encoded regions with a loss function based on gradient interpolation. We evaluate our method in a positioning task where a region map is used in order to reduce invalid position estimates. Results show that our approach is effective in decreasing invalid outputs for several geometrically complex environments.

Participatory approaches to the development of new practices at work have been widespread in Scandinavia, due largely to the tradition of collaboration and collective agreements on the labour market. Since the late 1980s, participation and change have increasingly been coupled to various notions of learning and learning organizations (for an overview, see Sandberg, 1992). Similarly, technological change became increasingly addressed as an issue of design rather than as a given precondition for changes in working life (Bjerknes et al., 1987). In the so-called Scandinavian tradition of systems design, IT systems for a particular customer organization are developed through a process of participatory design (Greenbaum & Kyng, 1991). Existing work practices are studied in a mixture of ethnographically inspired fieldwork, interviews and dialogue sessions. New IT systems are developed in iterative design cycles involving representative users in drafting and evaluating system prototypes. And a final system is typically put in place with the involved users acting as strong proponents for the chosen design. This tradition of user-oriented design of IT systems has shed new light on the relation between participation, learning and change and in particular the literature on computer supported cooperative work has contributed to the study of how practices at work evolve around communication artefacts. 

Background:

Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered “data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.

Findings:

In our pipeline model, an “interestingness function” assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a “policy” guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.

Conclusions:

Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems – and is intended for use with a range of technologies in different deployment scenarios.