The aim of the current study is to estimate whether spatial data on place features from OpenStreetMap (OSM) produce results similar to those when employing register data to predict future violent crime in public across three Swedish cities of varying sizes. Using violent crime in public as an outcome, four models for each city are produced using a Risk Terrain Modeling approach. One using spatial data on place features from register data and one from OSM, one model with prior violent crime excluded and one with prior crime included. The results show that several place features are significantly associated with violent crime in public independent of using register or OSM data as input. While models using register data seem to produce more accurate and efficient predictions than OSM data for the two smaller cities, the difference for the largest city is negligible indicating that the models provide similar results. As such, OSM place feature data may be of value when predicting the spatial distribution of future violent crime in public and provide results similar to those when using register data, at least when employed in larger compared to smaller cities. Possibilities, limitations, and avenues for future research when using OSM data in place-based criminological research are discussed. © The Author(s) 2024.

Under våren 2019 initierades en diskussion mellan Stefan Sinteus vid polisen i Malmö, Susanne Bäsk som VD för Länsförsäkringar Skåne samt Manne Gerell från Malmö Universitet om hur LF Skåne skulle kunna stötta ett projekt med fokus på trygghet i samhället. Utgångspunkten var att det saknas mycket kunskap om vad av det polisen gör som fungerar, och att det fanns ett behov av att stötta polisen med utvecklingsprojekt och utvärderingar. Den diskussionen utmynnade i en donation på fem miljoner kronor för att Malmö Universitet under 2020-2024 skulle kunna genomföra studier om brottsförebyggande och trygghetsskapande arbete. Arbetet har genomförts i samarbete med framför allt polisområde Malmö, men även andra delar av polismyndigheten samt länsstyrelsen och kommuner. En ambition har varit att ha en tvärvetenskaplig ansats, och projektet har under hela tiden inkluderat datavetenskapliga forskare från Blekinge Tekniska högskola. Under delar av projektet har också epidemiologen Carolina Ellberg, nationalekonomen Niklas Jakobsson samt kriminologen Alberto Chrysoulakis deltagit i projektet. 

Tanken var från början att på ett snabbt och lättillgängligt sätt kunna bistå polis och andra aktörer med svar på frågor från deras verksamhet av typen ”får den här insatsen den effekt vi är ute efter”? För att möjliggöra det har möten hållits halvårsvis med representanter för polisen i Malmö där det diskuterats vilka frågor som är intressanta att titta närmare på. Utifrån den diskussionen samt forskarnas egna idéer har projekt initierats. Vissa projekt har genomförts på en termin medan andra projekt dragit ut över flera år. En del projekt som har varit tänkta att genomföras har bortfallit av olika anledningar. Totalt handlar det om 16 delprojekt som initierats vilka kort sammanfattas i denna rapport. 

Generellt kan det konstateras att tydliga positiva effekter i många av studierna ej kan identifieras. Det är svårt att förebygga brott. Samtidigt kan det dock konstateras att de flesta studierna identifierar olika positiva aspekter på de insatser som görs ändå, t ex bättre underrättelser, fler beslagtagna vapen eller förbättrad samverkan. Studierna kommer nedan att presenteras kronologiskt, förutom avseende de mer datavetenskapliga studierna som tas upp i slutet. Inledningen av 2020 gick till stor del åt till att rekrytera personal, skriva samverkansavtal och liknande, men en större studie hann också genomföras under vårterminen. 

This paper studies the application of online federated learning for intrusion detection systems. An experiment is conducted with two different models, Gaussian Naive Bayes and Semi-supervised Federated Learning on Evolving Data Streams (SFLEDS), which are evaluated in four different settings, centralized offline, centralized online, federated offline, and federated online. The models are evaluated on the NSL-KDD dataset, and the federated models are run with 20,30, and 40 clients. The results show that for Naive Bayes, the centralized offline models have the best performance, while for SFLEDS, the federated online models perform the best with accuracy scores around 90%. Suggestions for improvements of the models are provided in the discussion, with the conclusion being that, while the results show promising results for federated online learning when employed for intrusion detection systems, the models used need to be carefully selected to achieve good results. Further research is also required for different models, such as deep learning models, which might achieve even better results. © 2024 IEEE.

In smart cities, Closed-Circuit Television (CCTV) cameras are crucial for enhancing law enforcement and public safety. Since CCTV cameras include both state-controlled and private-sector units in large numbers, it poses a considerable challenge to manage them. Automating the calculation of their coverage areas enables law enforcement and city planners to efficiently adapt surveillance strategies to the evolving needs of urban safety and dynamics.

This paper proposes a prototype that automates the digitization of a vast number of camera coverage areas, such as for a large city. Given the positions for each camera, its sector width, and length-of-view, the prototype automatically identifies each camera's capture area represented as a polygon of positions. The prototype's generated capture areas are validated to the ground truth representing the true capture areas for 51 cameras (in the city of Malmö, Sweden) provided by the Swedish law enforcement agencies. 

We present a method, including tool support, for bibliometric mining of trends in large and dynamic research areas. The method is applied to the machine learning research area for the years 2013 to 2022. A total number of 398,782 documents from Scopus were analyzed. A taxonomy containing 26 research directions within machine learning was defined by four experts with the help of a Python program and existing taxonomies. The trends in terms of productivity, growth rate, and citations were analyzed for the research directions in the taxonomy. Our results show that the two directions, Applications and Algorithms, are the largest, and that the direction Convolutional Neural Networks is the one that grows the fastest and has the highest average number of citations per document. It also turns out that there is a clear correlation between the growth rate and the average number of citations per document, i.e., documents in fast-growing research directions have more citations. The trends for machine learning research in four geographic regions (North America, Europe, the BRICS countries, and The Rest of the World) were also analyzed. The number of documents during the time period considered is approximately the same for all regions. BRICS has the highest growth rate, and, on average, North America has the highest number of citations per document. Using our tool and method, we expect that one could perform a similar study in some other large and dynamic research area in a relatively short time.

Preventing customer churn, i.e., termination of business commitments, is essential for companies operating in saturated markets, especially for subscription-based models such as telecommunication. Knowing when customers decide to terminate services is instrumental to effective churn prevention. In this study, we investigate how churn prediction performs in practice when training models on different time intervals of historic data (1-4 weeks back) and predicting churn at different numbers of weeks ahead (1-4 weeks). We use a real-world, time-series dataset of mobile subscription usage to examine churn prediction for business-to-business (B2B) customers. We utilize the timeseries data at a higher temporal resolution than prior studies and investigate different forecasting horizons. Leveraging popular machine learning algorithms such as Random Forests, Gradient Boosting, Neural Networks, and Gated Recurrent Unit, we show that the best model achieves an average F1-score of 79.3% for one-week ahead predictions. However, the average F1-score decreases to 63.3% and 61.8% for two and four weeks ahead, respectively. A model interpretation framework (SHAP) evaluates the feature impact on the models' internal decision logic. We also discuss the challenges in applying churn prediction for the B2B segment. 

This paper investigates the impact that datasets of varying data quality levels have on centralized vs. federated learning models using experiments. We also investigate how the distribution of low-quality data across federated clients affects the models' accuracy. Within the experiments we create datasets of increasingly worse data quality in terms of the following two data quality metrics; data accuracy and data completeness. This is done by perturbing (i.e., modifying) the datasets in order to decrease the quality of the datasets with regard to these two data quality metrics. Then, three experiments are conducted that investigates; i) the impact of decreased data accuracy on the models' performance, ii) the impact of decreased data completeness, and iii) the effects of different distribution low-quality data on the clients used in the federated learning setup. The results reveal that the centralized model achieves 60.3% validation accuracy with low data accuracy and 58.7% with low data completeness. While the federated model performs better, achieving 69.3% validation accuracy with low data accuracy and 79.2% with low data completeness. The federated model is less affected by low data quality if the data quality is distributed evenly between its clients. Further, the federated learning setup displays certain attributes that make it more robust to data with low quality, compared to centralized learning. Uneven distribution of data quality between clients has a more negative impact on federated learning compared to even distribution. © 2024 IEEE.

The one-pixel attack is an image attack method for creating adversarial instances with minimal perturbations, i.e., pixel modification. The attack method makes the adversarial instances difficult to detect as it only manipulates a single pixel in the image. In this paper, we study four different defense approaches against adversarial attacks, and more specifically the one-pixel attack, over three different models. The defense methods used are: data augmentation, spatial smoothing, and Gaussian data augmentation used during both training and testing. The empirical experiments involve the following three models: all convolutional network (CNN), network in network (NiN), and the convolutional neural network VGG16. Experiments were executed and the results show that Gaussian data augmentation performs quite poorly when applied during the prediction phase. When used during the training phase, we see a reduction in the number of instances that could be perturbed by the NiN model. However, the CNN model shows an overall significantly worse performance compared to no defense technique. Spatial smoothing shows an ability to reduce the effectiveness of the one-pixel attack, and it is on average able to defend against half of the adversarial examples. Data augmentation also shows promising results, reducing the number of successfully perturbed images for both the CNN and NiN models. However, data augmentation leads to slightly worse overall model performance for the NiN and VGG16 models. Interestingly, it significantly improves the performance for the CNN model. We conclude that the most suitable defense is dependent on the model used. For the CNN model, our results indicate that a combination of data augmentation and spatial smoothing is a suitable defense setup. For the NiN and VGG16 models, a combination of Gaussian data augmentation together with spatial smoothing is more promising. Finally, the experiments indicate that applying Gaussian noise during the prediction phase is not a workable defense against the one-pixel attack. ©2023, Copyright held by the authors   

During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However,implementing and generating value from AI in the customerlife cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both themodel performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer lifecycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regardingoutcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating bothpolicy outputs and outcomes to ensure reliable and trustworthy AI.