We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. 

The lubrication and tension levels are two important parameters when running a chainsaw, both impacting the cutting performance and lifespan of the tool. An appropriate level of lubrication and tension leads to maximum performance of the chainsaw and direct benefits for the end-user. This thesis addresses the problem of detecting the lubrication status and the tension level using the information contained in the acoustic emissions captured in the guidebar of the chainsaw. Data was collected by running controlled experiments using an acoustic emissions sensor.Information was extracted from acoustic emissions by using a number of features computed on different frequency ranges.Three machine learning models were trained and evaluated on data corresponding to different combinations of lubrication status and tension levels. The models' performances were evaluated using the well-known metrics accuracy, precision, and recall. A pattern was found for each lubrication and tension setup, and the model that registered the highest performance was the Random Forest. The impact of temperature, guidebar, and chain on acoustic emissions is also analyzed. The detection of different lubrication levels using the information contained by acoustic signals is also addressed, the patterns in data being determined by computing features in the time and frequency domains. The analysis shows that the temperature does not have an impact when the running time is less than 10 minutes, and the chain has a bigger impact than the guidebar for the specific setup of the experiments. Moreover, a pattern dependent on the guidebar and chain combination correlated with the lubrication level was identified. The main contribution of this thesis consists of detecting a pattern representative of lubrication and tension setup in acoustic emission using a number of features computed in different frequency ranges.

Rules output by explainable machine learning techniques naturally come with a degree of uncertainty, as the complex functionality of the underlying black-box model often can be difficult to approximate by a single, interpretable rule. However, the uncertainty of these approximations is not properly quantified by current explanatory techniques. The use of Venn prediction is here proposed and investigated as a means to quantify the uncertainty of the explanations and thereby also allow for competing explanation techniques to be evaluated with respect to their relative uncertainty. A number of metrics of rule explanation quality based on uncertainty are proposed and discussed, including metrics that capture the tendency of the explanations to predict the correct outcome of a black-box model on new instances, how informative (tight) the produced intervals are, and how certain a rule is when predicting one class. An empirical investigation is presented, in which explanations produced by the state-of-the-art technique Anchors are compared to explanatory rules obtained from association rule mining. The results suggest that the association rule mining approach may provide explanations with less uncertainty towards the correct label, as predicted by the black-box model, compared to Anchors. The results also show that the explanatory rules obtained through association rule mining result in tighter intervals and are closer to either one or zero compared to Anchors, i.e., they are more certain towards a specific class label.

There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but most of them have focused on single aspects of postural control. This thesis has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. We performed a data collection with 13 human participants who carried out live rifle shooting scenarios while being recorded with multiple biometric sensors, including several body trackers. An experiment was conducted to identify what aspects of rifle shooting could be predicted and explained using these data. We employed a pre-processing pipeline to produce a novel skeleton sequence representation, and used it to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (81%). However, no correlation could be shown between shooting performance and body posture from our data. Future work could focus on novel feature engineering, and on examining alternative machine learning approaches. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this thesis has laid the groundwork for future research in the sports shooting domain.

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Recommendation systems in fashion are used to provide recommendations to users on clothing items, matching styles, and size or fit. These recommendations are generated based on user actions such as ratings, reviews or general interaction with a seller. There is an increased adoption of implicit feedback in models aimed at providing recommendations in fashion. This paper aims to understand the nature of implicit user feedback in fashion recommendation systems by following guidelines to group user actions. Categories of user actions that characterize implicit feedback are examination, retention, reference, and annotation. Each category describes a specific set of actions a user takes. It is observed that fashion recommendations using implicit user feedback mostly rely on retention as a user action to provide recommendations.

This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.

Bluetooth Low Energy (BLE) is a widely used low-energy version of Bluetooth’swireless protocol. To meet increasing requirements of modern wireless audio devices,Bluetooth LE Audio was released with its new Low Complexity CommunicationsCodec (LC3) being much more data efficient than its predecessor Low Complexity SubBand Coding.

Because of its increased data efficiency, LC3 opens the door of exploring usage ofvarious physical layer configurations, especially those with lower data rates. Thedifference in performance when streaming audio with the uncoded LE 2M and 1Mconfigurations, compared to using the LE coded S=2 and S=8 configurations (whichhave a lower throughput) points to a research gap which this thesis aims to fill.

To be able to gather data necessary to fill the identified gap, multiple iterations of bothsoftware and hardware artefacts were made. The produced artefacts were designed torun the same Bluetooth version (LE Audio) and switch between the physical layerconfigurations. Throughput and current consumption in varied ranges was measuredthrough usage of the artefacts.

The results from the experiments show that for energy optimization, an adaptive schemewould not be beneficial over only using LE 2M. However, an adaptive scheme for thephysical layer can be used for LE Audio to improve range and stability. This doeshowever, come with the cost of increased energy consumption.

Polandball comics are hand-drawn satirical content that portray personified countries in a unique style. Although certain parts of these comics, such as ball outlines, are easy to draw, some country flags are complex and require time, effort, and skill to depict correctly. Convolutional Neural Networks have shown success in image synthesis tasks but lack the ability to rescale and rotate images for texture mapping. The domain of Virtual Try-On Networks has made great progress in networks that can handle spatially invariant transforms. We show that similar methods can be used in another domain dependent on texture mapping, namely generating valid, rule-abiding Poland-ball characters given an outline and a country flag. To evaluate our method we make use of the Fréchet Inception Distance where we achieved a score of 34.9. Multiple configurations of the model were evaluated to show that all modules used in the model contribute to the achieved performance. The main contributions in this paper are: a model that can be used by Polandball artists to aid in comic creation and a dataset with over 40,000 labeled Polandball characters for computer vision tasks.

Introduction: Number of strata (NoS) is an informative descriptor of forest structure and is therefore useful in forest management. Collection of NoS as well as other forest properties is performed by fieldworkers and could benefit from automation.

Objectives: This study investigates automated prediction of NoS from airborne laser scanned point clouds over Swedish forest plots.Methods: A previously suggested approach of using vertical gap probability is compared through experimentation against the geometric neural network PointNet++ configured for ordinal prediction. For both approaches, the mean accuracy is measured for three datasets: coniferous forest, deciduous forest, and a combination of all forests.

Results: PointNet++ displayed a better point performance for two out of three datasets, attaining a top mean accuracy of 46.2%. However only the coniferous subset displayed a statistically significant superiority for PointNet++.

Conclusion: This study demonstrates the potential of geometric neural networks for data mining of forest properties. The results show that impediments in the data may need to be addressed for further improvements.

Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.

Physiological markers have long been used to monitor physiological state in individual athletes. More recently, heart rate variability (HRV) has become a popular metric to monitor athletes' physiological state over longer periods of time to guide training and detect fatigue. HRV measured immediately prior to shooting has been shown to be a predictor of shooting performance. However, there is a lack of research on how physiological state as measured by HRV in resting states impacts sports shooting performance over longer periods of time. This thesis explored if there was a relationship between HRV and rifle shooting performance through a six-week-long experiment. Ten participants wore wrist sensors that measured HRV during slow wave sleep and performed simulator rifle shooting tasks twice a week to measure shooting performance. The relationship between HRV and shooting performance was analyzed through Pearson’s correlation coefficient, linear regression, and k-means clustering. The results indicated that there was no relationship between HRV and shooting performance in the participants collectively, except for two participants. The thesis contributed to the current knowledge about physiological state and HRV in relation to sports shooting performance. It also gave new insight into how experiments can be designed to study variability of physiological state in relation to shooting performance over longer periods of time.

Autonomous robots are becoming a bigger part of our society. This thesis aims to evaluate a robot testing dashboard (RTD) that can be used as a new way of finding improvements when developing autonomous robots that do not use machine learning.

The method that is used is design science research, which is used when creating and evaluating an artifact to address a practical problem. In our case the artifact isthe RTD.

This project was performed at a company called Greenworks, which among other things develops and sells autonomous lawn mowers. The company wants to find new testing methods to help develop their autonomous lawnmowers. The RTD is created to visualize the inputs that the lawn mower utilizes to perform its tasks. A human pilot will then control the lawn mower, by only looking at that visualized data. If the pilot using the RTD can execute the same tasks as the lawn mower in its autonomous mode, the test results can be analyzed to see whether the human has done some parts of the tasks differently. The best outcome from the analysis of the test results is to find areas of improvement that can be implemented into the autonomous lawn mower design, both in software and hardware.

For this purpose, an RTD was built and tested at Greenworks. From the tests using the RTD we concluded that it is helpful in the testing process, and we could find areas of improvements after analysis of our tests. However, the use of the RTD will require more time and resources compared to other methods. Each company that uses a similar dashboard concept will have to evaluate if the benefits are worth the time. Furthermore, the concept may not suit all areas of robotics but does seem to suit situations where a human can have an advantage over robots, such as in creative problem solving.

Ontologies are the corner stone of data modeling and knowledge representation, and engineering an ontology is a complex task in which domain knowledge, ontological accuracy and computational properties need to be carefully balanced. As with any engineering task, the identification and documentation of common patterns is important, and Ontology Design Patterns (ODPs) provide ontology designers with a strong connection to requirements and a better communication of their semantic content and intent.

This book, Advances in Pattern-Based Ontology Engineering, contains 23 extended versions of selected papers presented at the annual Workshop on Ontology Design and Patterns (WOP) between 2017 and 2020. This yearly event, which attracts a large number of researchers and professionals in the field of ontology engineering and ontology design patterns, covers issues related to quality aspects of ontology engineering and ODPs for data and knowledge representation, and is usually co-located with the International Semantic Web Conference (ISWC), apart from WOP 2020, which was held virtually due to the COVID-19 pandemic. Topics covered by the papers collected here focus on recent advances in ontology design and patterns, and range from a method to instantiate content patterns, through a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations and applications.

The book provides an overview of important advances in ontology engineering and ontology design patterns, and will be of interest to all those working in the field.

We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios. Unsuccessfully. In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams. We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.

The distributions output by a standard (non-normalized) conformal predictive system all have the same shape but differ in location, while a normalized conformal predictive system outputs distributions that differ also in shape, through rescaling. An approach to further increasing the flexibility of the framework is proposed, called Mondrian conformal predictive distributions, which are (standard or normalized) conformal predictive distributions formed from multiple Mondrian categories. The effectiveness of the approach is demonstrated with an application to regression forests. By forming categories through binning of the predictions, it is shown that for this model class, the use of Mondrian conformal predictive distributions significantly outperforms the use of both standard and normalized conformal predictive distributions with respect to the continuous- ranked probability score. It is further shown that the use of Mondrian conformal predictive distributions results in as tight prediction intervals as produced by normalized conformal regressors, while improving upon the point predictions of the underlying regression forest.

Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.

Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot's raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles. A video of our experiments can be found at: https://youtu.be/rUdGPKr2Wuo.

Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data from real robotics hardware is usually very high, thus solutions that achieve high sample-efficiency are needed. We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. Using the samples from the state space, the optimal policy is quickly and efficiently learned. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot. A video of our experiments can be found at: https://youtu.be/dgWxmfSv95U.

Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn - ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.

Autonomous robots struggle with plan adaption in uncertain and changing environments. Although modern robots can make popcorn and pancakes, they are incapable of performing such tasks in unknown settings and unable to adapt action plans if ingredients or tools are missing. Humans are continuously aware of their surroundings. For robotic agents, real-time state updating is time-consuming and other methods for failure handling are required. Taking inspiration from human cognition, we propose a plan adaption method based on event segmentation of the image-schematic states of subtasks within action descriptors. For this, we reuse action plans of the robotic architecture CRAM and ontologically model the involved objects and image-schematic states of the action descriptor cutting. Our evaluation uses a robot simulation of the task of cutting bread and demonstrates that the system can reason about possible solutions to unexpected failures regarding tool use.

Optimising object order in stacking problems remains a hard problem for cognitive robotics research. In this paper, we continue our work on using the spatiotemporal relationships called image schemas to represent affordance spaces founded on object properties. Based on object properties, we introduce a stacking-order algorithm and describe the action descriptors using an image-schematic event segmentation format by describing a small subset using the Image Schema Logic ISL^𝐹𝑂𝐿.

In this extended abstract, we present initial work on intelligent object stacking by household robots using a symbolic approach grounded in image schema research. Image schemas represent spatiotemporal relationships that capture objects’ affordances and dispositions. Therefore, they offer the first step to ground semantic information in symbolic descriptions. We hypothesise that for a robot to successfully stack objects of different dispositions, these relationships can be used to more intelligently identify both task constraints and relevant event segments. 

Discovering the crucial factors that contribute to goal success in sports analytics, this thesis aimsto utilize Random Forest classification to predict the outcome of shots and pre-shot events in powerplay situations. Through three experiments, the study evaluated the use of shots, shots with pre-shotevents, and shots with pre-shot events over sections. The first experiment used only shots, while thesecond experiment focused on shots with pre-shot events, where both compared it with shots over anexpected goal value of 0.08 or higher. The third experiment examined shots with pre-shot events acrossdifferent sections. Our findings demonstrated that the models in our experiments achieved accuracyscores ranging from 78% to 96% and F1 scores between 0% and 24%. Notably, the models in experiment3 demonstrated lower recall scores. The feature importance analysis revealed that pre-shotevents played a significant role in the predictive models of the second and third experiments, indicatingtheir substantial impact on the outcomes. A noteworthy conclusion arising from the discussion isthe recommendation for future research to conduct a more comprehensive exploration into the impactof pre-shot events, given their demonstrated significance in predicting goals. Such an investigation isdeemed necessary and justified.

Fashion history is an integral subfield of history as a whole. Fashion plates provide important evidence of what fashion once looked like and as such are a valuable window into the lives of the people in ages past. The rise of digitization opens new avenues for aiding historians in the dating of fashion plates; following on from this, digitization also brings a greater need for artworks to be digitized and AI can be utilized in order to keep up with the demand. This provides unique challenges such as gathering data and working with a relatively limited database. Due to the lack of prior research into the subject of dating fashion plates using Artificial Intelligence (AI), said application of AI in the dating process could help future historians automate the task. Transfer learning can help streamline the dating process of fashion plates. I used several approaches with three different models (ResNet101, NasNetMobile, and InceptionV3) and achieved the best mean absolute error of 2.8 years in a range of 60 years using NasNetMobile with a simple output layer and no fine-tuning.

There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but have mostly focused on single aspects of postural control. This study has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. A data collection was performed with 13 human participants carrying out live rifle shooting scenarios while being recorded with multiple body tracking sensors. A pre-processing pipeline produced a novel skeleton sequence representation, which was used to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (80%). Shooting performance could be detected to an extent by separating participants using their strong and weak shooting hand. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this study have laid the groundwork for future research in the sports shooting domain.

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average.

Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

Managing campaigns and promotions effectively is vital for the fashion retail industry. While retailers invest a lot of money in campaigns, customer retention is often very low. At innovative retailers, data-driven methods, aimed at understanding and ultimately optimizing campaigns are introduced. In this application paper, machine learning techniques are employed to analyze data about campaigns and promotions from a leading Swedish e-retailer. More specifically, predictive modeling is used to forecast the profitability and activation of campaigns using different kinds of promotions. In the empirical investigation, regression models are generated to estimate the profitability, and classification models are used to predict the overall success of the campaigns. In both cases, random forests are compared to individual tree models. As expected, the more complex ensembles are more accurate, but the usage of interpretable tree models makes it possible to analyze the underlying relationships, simply by inspecting the trees. In conclusion, the accuracy of the predictive models must be deemed high enough to make these data-driven methods attractive.

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related frameworks that provide abstractions of the problems a visualization is meant to address. In this Visualization Viewpoints article, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations. 

In the current studied literature there is a lack of data and documentation on how outside factors affect the performance of ANC headphones. The purpose of this thesis is to investigate how these factors affect ANC headphone performance. The investigation into these factors was done by gathering quantitative data with experiments. The analysis method chosen was an ANOVA-inspired method that compared the differences found in the processed data.

The results show that the sample variation between the number of speakers used did not differ by much. However, the variability in the samples collected using different noise types was noticeably higher while short noise resulted in odd-shaped graphs. When combining parameters from the different tests, effects such as graph shapes, seem to remain.

These are the conclusions made. When testing with pink noise, the amount of speakers used and how long the pink noise is played makes a minimal difference for attenuation and variation. However, having more speakers and playing the sound for longer durations creates slightly more stable measurements. Unstable noise recordings are unsuitable for product quality testing due to the unreliable results they give. These noise recordings are still suitable for other research purposes.

This study has put three different object detecting techniques to the test. The goal was to investigate small and fast-moving objects to see which technique’s performance is most suitable within the sports of Padel. The study aims to cover and explain different affecting conditions that could cause better but also worse performance for small and fast object detection. The three techniques use different approaches for detecting one or multiple objects and could be a guideline for future object detection development. The proposed techniques utilize background histogram calculation, HSV masking with edge detection and DNN frameworks together with the COCO dataset. The process is tested through outdoor video footage across all techniques to generate data, which indicates that Canny edge detection is a prominent suggestion for further research given its high detection rate. However, YOLO shows excellent potential for multiple object detection at a very high confidence grade, which provides reliable and accurate detection of a targeted object. This study’s conclusion is that depending on what the end purpose aims to achieve, Canny and YOLO have potential for future small and fast object detection.

Introduction

This study aims to investigate the possibility of driving multiple NFC antennas with a single NFC-controller by using a multiplexer. An artifact was developed and experiments were conducted in order to evaluate the design choice and in addition, to observe the impact of a multiplexer in an NFC system with respect to the number of successful tag detections, bit error rate (BER), the utilization of automatic antenna tuning (AAT) and the physical distance to the NFC tag. Data collected from the experiments was analyzed, and the results were discussed. The purpose of the thesis was concretized by formulating three research questions:

[RQ1] How can the design proposed in the problem statement be implemented?

[RQ2] How does the multiplexer impact the system’s ability to detect an NFC tag and read its contents at different distances?

[RQ3] To which degree does the AAT-technology compensate for the issues caused by the multiplexer?

Method

This research has been conducted according to the design science research (DSR) methodology. DSR is a well-established methodology and suits the nature of this research, therefore it is used to answer the research questions.

Findings

The thesis demonstrates a viable solution using a multiplexer in NFC systems, with reduced detection distance as a factor to consider. The impact of AAT remains inconclusive, requiring further research.

Implications

This study shows that using a multiplexer in NFC systems can save costs and space, leading to more affordable and compact devices. It enables scalable, advanced NFC devices for diverse applications, encouraging wider adoption. However, developers must consider the impact of a multiplexer on detection distance when designing NFC-based applications

Limitations

This study has certain limitations. It focuses on a particular multiplexer and NFC antenna type, and specific testing conditions. It does not delve into the potential profitability, diverse applications, security or encryption concerns, compatibility with other devices, compliance with regulations, or hands-on verification of part connectivity.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future.

Introductory paragraph: Invited to participate in the documentation project of .co.kr in the %wrong Browser series, I was asked to – to the best of my professional ability – enable the experiencing of a digital art piece in a future when technological advancements and paradigm shifts made it impossible access. Drawing on my scientifc background, my usual research method is to formally structure, identify and analyze semantic micro-patterns of concepts and events with the goal of integrating them into formal systems for artifcial intelligence. Approaching the task in this way, I performed what for lack of a better word could be called a “documentalysis” (the amalgamation of documentation and analysis) on the art piece. This was a form of interactive, experience-based documentation in which I was trying to separate syntactical parts of the art piece and analyze their semantic content.

Image schemas have been proposed to be the conceptual building blocks that constitute the semantic skeleton for concepts, events and narratives. Learned from embodied experiences, they encompass abstract information in notions such as Containment, Source_Path_Goal and Scale. The theory originated from cognitive linguistics as a means to explain the vast prevalence of embodied metaphors and spatial language. However, it has become a valid contribution in many areas investigating the nature of thought. For formal analysis of image schemas, the abstract and undetermined notions in the image schemas require precise and concrete representations. To deal with this, the image schemas can be decomposed into different types of conceptual primitives. By adding or removing these primitives, the schematic narrative changes in subtle but essential ways. To demonstrate the power of using a methodology that isolates these primitives, this paper presents a formal analysis of the transfer of forces and motion by looking at the semantic differences in a selected number of synonyms for ‘pushing’. The analysis is done using the visualisation tool The Diagrammatic Image Schema Language and the notions are formalised using The Image Schema Logic.