Adversarial Machine Learning (AML) discusses the act of attacking and defending Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML is applied in many software-intensive products and services and introduces new opportunities and security challenges. AI and ML will gain even more attention from the industry in the future, but threats caused by already-discovered attacks specifically targeting ML models are either overseen, ignored, or mishandled. Current AML research investigates attack and defense scenarios for ML in different industrial settings with a varying degree of maturity with regard to academic rigor and practical relevance. However, to the best of our knowledge, a synthesis of the state of academic rigor and practical relevance is missing. This literature study reviews studies in the area of AML in the context of industry, measuring and analyzing each study's rigor and relevance scores. Overall, all studies scored a high rigor score and a low relevance score, indicating that the studies are thoroughly designed and documented but miss the opportunity to include touch points relatable for practitioners. © 2024 The Author(s)

This study introduces a novel systematic approach to address the challenge of labeled data scarcity for fault detection and diagnosis (FDD) in District Heating (DH) systems. To replicate real-world DH fault scenarios, we have created a controlled laboratory emulation of a generic DH substation integrated with a climate chamber. Furthermore, we present an FDD pipeline using an isolation forest and a one-class support vector machine for fault detection alongside a random forest and a support vector machine for fault diagnosis. Our research analyzed the impact of data sampling frequencies on the FDD models, revealing that shorter intervals, such as 1-min and 5-min, significantly improve FDD performance. We provide detailed information on six scenarios, including normal operation, a minor valve leak, a valve leak, a stuck valve, a high heat curve, and a temperature sensor deviation. For each scenario, we present their signature, quantifying their unique behavior and providing deeper insights into the operational implications. The signatures suggest that, while variable, faults have a consistent pattern seen in the generic DH substation. While this work contributes directly to the DH field, our methodology also extends its applicability to a broader context where labeled data is scarce. © 2024 The Authors

The research presented in the paper evaluates practices of Attribute-Based Encryption, leading to a proposed end-to-end encryption strategy for a cloud-based drone management system. Though extensively used for efficiently gathering and sharing video surveilance data, these systems also collect telemetry information with sensitive data. This paper presents a study addressing the current state of knowledge, methodologies, and challenges associated with supporting cryptographic agility for End-to-End Encryption (E2EE) for telemetry data confidentiality. To enhance cryptographic agility performance, a new metric has been introduced for cryptographic library analysis that improves the methodology by considering Attribute-Based Encryption (ABE) with a conventional key-encapsulation mechanism in OpenSSL. A comprehensive series of experiments are undertaken to simulate cryptographic agility within the proposed system, showcasing the practical applicability of the proposed approach in measuring cryptographic agility performance. 

Using a novel method and tool in the form of a Python program, we present a bibliometric study based on 46,937 documents related to smart cities from the Scopus database. The study identifies important research directions and trends during the time period 2014 to 2023. We also present the growth of smart city research for five geographic regions. Citation analysis for research directions and regions is also performed. The results show that smart city research in general stopped growing around 2019. However, some research directions are still growing, e.g., smart city research related to machine learning and AI. India is the only geographic region where smart city research still is growing. We also see that the number of citations of a smart city document from North America is on average a factor 3.74 larger than the number of citations to a document from India. © 2024 IEEE.

Despite the accuracy of machine learning (ML) techniques in predicting invalid bug reports, as shown in earlier research, and the importance of early identification of invalid bug reports in software maintenance, the adoption of ML techniques for this task in industrial practice is yet to be investigated. In this study, we used a technology transfer model to guide the adoption of an ML technique at a company for the early identification of invalid bug reports. In the process, we also identify necessary conditions for adopting such techniques in practice. We followed a case study research approach with various design and analysis iterations for technology transfer activities. We collected data from bug repositories, through focus groups, a questionnaire, and a presentation and feedback session with an expert. As expected, we found that an ML technique can identify invalid bug reports with acceptable accuracy at an early stage. However, the technique’s accuracy drops over time in its operational use due to changes in the product, the used technologies, or the development organization. Such changes may require retraining the ML model. During validation, practitioners highlighted the need to understand the ML technique’s predictions to trust the predictions. We found that a visual (using a state-of-the-art ML interpretation framework) and descriptive explanation of the prediction increases the trustability of the technique compared to just presenting the results of the validity predictions. We conclude that trustability, integration with the existing toolchain, and maintaining the techniques’ accuracy over time are critical for increasing the likelihood of adoption. © The Author(s) 2024.

Opinion mining has witnessed significant advancements in well-resourced languages. However, for low-resource languages, this landscape remains relatively unexplored. This paper addresses this gap by conducting a comprehensive investigation into sentiment analysis in the context of Hausa, one of the most widely spoken languages within the Afro-Asiatic family. To resolve the problem, three different models based on Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hierarchical Attention Network (HAN), all tailored to the unique linguistic characteristics of Hausa have been proposed. Additionally, we have developed the first dedicated lexicon dictionary for Hausa sentiment analysis and a customized stemming method to enhance the accuracy of the bag of words approach. Our results indicate that CNN and HAN achieved significantly higher performance compared to other models such as RNN. While the experimental results demonstrate the effectiveness of the developed deep learning models in contrast to the bag of words approach, the proposed stemming method was found to significantly improve the performance of the bag of words approach. The findings from this study not only enrich the sentiment analysis domain for Hausa but also provide a foundation for future research endeavors in similarly underrepresented languages. © 2023 IEEE.

The aim of the study was to explore students' own translation of the symbolic level of a chemical reaction, including the information provided with the use of coefficients, indices, and signs, as well as the preservation of atoms. Students were asked to translate the symbolic level of the combustion of methane with the use of clay modelling. The students had to make active choices regarding the size, shape, two- or three-dimensional structure, and the number of atoms in the molecules included in the reaction using modelling clay. The analysis followed the three levels of analysis as presented by Hedegaard. The results highlight the variations in students' answers and show the importance of investigating unrestricted translations of the symbolic level of chemistry. Including clay modelling in the educational process is helpful for both educators and students, as it fosters comprehension of underlying processes and enhances awareness of substance structure and atom redistribution across various substances.

WebGL has been the standard API for rendering graphics on the web over the years. A new technology, WebGPU, has been set to release in 2023 and utilizes many of the novel rendering approaches and features common for the native modern graphics APIs, such as Vulkan. Currently, very limited research exists regarding WebGPU's rasterization capabilities. In particular, no research exists about its capabilities when used as a rendering backend in game engines. This paper aims to investigate performance differences between WebGL and WebGPU. It is done in the context of the game engine Godot, and the measured performance is that of the CPU and GPU frame time. The results show that WebGPU performs better than WebGL when used as a rendering backend in Godot, for both the games tests and the synthetic tests. The comparisons clearly show that WebGPU performs faster in mean CPU and GPU frame time. © 2024 IEEE.

The success of Federated Learning (FL) hinges upon the active participation and contributions of edge devices as they collaboratively train a global model while preserving data privacy. Understanding the behavior of individual clients within the FL framework is essential for enhancing model performance, ensuring system reliability, and protecting data privacy. However, analyzing client behavior poses a significant challenge due to the decentralized nature of FL, the variety of participating devices, and the complex interplay between client models throughout the training process. This research proposes a novel approach based on eccentricity analysis to address the challenges associated with understanding the different clients' behavior in the federation. We study how the eccentricity analysis can be applied to monitor the clients' behaviors through the training process by assessing the eccentricity metrics of clients' local models and clients' data representation in the global model. The Kendall ranking method is used for evaluating the correlations between the defined eccentricity metrics and the clients' benefit from the federation and influence on the federation, respectively. Our initial experiments on a publicly available data set demonstrate that the defined eccentricity measures can provide valuable information for monitoring the clients' behavior and eventually identify clients with deviating behavioral patterns. © 2024 IEEE.

This paper applies spatial and temporal adaptivity to an existing discrete element method (DEM) based snow simulation on the GPU. For spatial adaptivity, visually significant spatial regions are identified and simulated at varying resolutions. To this end, we propose efficient splitting and merging to generate adaptive resolution while maintaining the simulation stability. We obtain further optimization by skipping computation on temporally inactive regions. In agreement with the base solver, our method also operates almost entirely on the GPU, which includes operations like activity determination, merging, and splitting of the particles. We demonstrate that a speedup of three times or more of the original non-adaptive simulation can be achieved on scenes containing about 3 million particles. We also discuss the advantages and drawbacks of our spatiotemporal optimization in different simulation scenarios.