Background: Modern Code Review (MCR) is a key component for delivering high-quality software and sharing knowledge among developers. Effective reviews require an in-depth understanding of the code and demand from the reviewers to contextualize the change from different perspectives.

Aim: While there is a plethora of research on solutions that support developers to understand changed code, we have observed that many provide only narrow, specialized insights and very few aggregate information in a meaningful manner. Therefore, we aim to provide a vision of improving code understanding in MCR.

Method: We classified 53 research papers suggesting proposals to improve MCR code understanding. We use this classification, the needs expressed by code reviewers from previous research, and the information we have not found in the literature for extrapolation.

Results: We identified four major types of support systems and suggest an environment for contextualized code reviews. Furthermore, we illustrate with a set of scenarios how such an environment would improve the effectiveness of code reviews.

Conclusions: Current research focuses mostly on providing narrow support for developers. We outline a vision for how MCR can be improved by using context and reducing the cognitive load on developers. We hope our vision can foster future advancements in development environments. 

With the escalating complexity and range of regu-lations impacting the development and operations of software-intensive systems, engineers are compelled to manage intensifying regulatory oversight. The critical task of analyzing and interpreting regulatory norms, as well as deriving software requirements, is a vital step in achieving regulatory compliance. Nevertheless, the interpretation of regulations remains heavily reliant on the individual expertise and domain-specific experience of legal professionals, with a notable absence of systematic methodologies and supportive tools to streamline this process. Research in this domain frequently remains isolated from the practical experiences of industry practitioners, resulting in solutions that struggle to find relevance in real-world applications. The work outlines a doctoral thesis aiming to have a detailed examination of the existing state of reported evidence in RE related to regulatory compliance and, analysis of current practices and obstacles in practice, to identify key areas for improvement and development of supportive tools and methodologies. Furthermore, this work includes an investigation into the limitations and potentials of automation in crafting viable approaches for regulatory RE. The ultimate goal is to bridge the theoretical and practical aspects of regulatory RE, ensuring the creation of a tool-supported approach that is both academically robust and pragmatically applicable. By focusing on enhancing the structure and utility of RE practices in the face of regulatory demands, this work seeks to contribute to the field, paving the way for more effective compliance management in software engineering. © 2024 IEEE.

Syftet med denna rapport är att studera betydelsen av unga växande företag i Sverige underperioden 2003 till 2022. Vi studerar relationen mellan tillväxt i antal anställda och företagensålder och storlek med hjälp av registerdata över svenska privata aktiebolag. Mer specifiktbesvarar vi följande frågeställningar: Vilken betydelse har de unga företagen för jobbtillväxten i Sverige? Hur har nyföretagande och unga snabbväxande företag utvecklats över tid? Bör politiken fokusera på att stimulera framväxt av nya företag i Sverige och vilkareformer är effektiva för att utveckla ett dynamiskt näringsliv i Sverige med fler ungaväxande företag?

This paper pays homage to Professor Borje Johansson's scientific contributions in general and to the field of regional science particularly through a bibliometric approach, highlighting his publishing and citation record, co-authors and co-editors, as well as his theoretical inspirators, fellow researchers and often cited researchers.

The concept of the Human Digital Twin (HDT) has recently emerged as a new research area within the domain of digital twin technology. HDT refers to the replica of a physical-world human in the digital world. Currently, research on HDT is still in its early stages, with a lack of comprehensive and in-depth analysis from the perspectives of universal frameworks, core technologies, and applications. Therefore, this paper conducts an extensive literature review on HDT research, analyzing the underlying technologies and establishing typical frameworks in which the core HDT functions or components are organized. Based on the findings from the aforementioned work, the paper proposes a generic architecture for the HDT system and describes the core function blocks and corresponding technologies. Subsequently, the paper presents the state of the art of HDT technologies and their applications in the healthcare, industry, and daily life domains. Finally, the paper discusses various issues related to the development of HDT and points out the trends and challenges of future HDT research and development.

In the era of Industry 4.0, fostering innovation through strong collaboration among universities, industry, and government is crucial. However, the Tanzanian economy has faced challenges due to, among others, weak links between these entities. To address this, Sida initiated the Innovation Systems and Cluster Development Program (ISCP-Tz) in partnership with the University of Dar es Salaam. This was followed by a program at COSTECH focused on fostering innovation for socio-economic development. As part of these programs, 15 clusters, including KIWANGO Leather, were selected for documentation of their experiences. KIWANGO Leather exemplifies successful collaboration between cluster firms, the university, local government, and research institutions. Operationalizing guidelines for collaboration led to cooperative innovations, knowledge exchange, internships, and long-term partnerships established through Memorandum of Understanding (MoU). The cluster's experiences highlight a less-linear, inclusive innovation process with positive outcomes. Thus, this paper not only illustrates a less linear, inclusive innovation process, but also its results can motivate actors in emerging innovation ecosystems in the Global South to adopt and scale up suggested approaches for knowledge co-creation benefitting sustainable development. By adopting these approaches and fostering collaborative networks, countries can leverage their resources and expertise to drive innovation, create economic growth, and address societal challenges.

Background

Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.

Objective

The proposed study aims to explore whether the voice features extracted from the vowel “a” utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset.

Methods

Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel “a” utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures.

Results

The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order.

Conclusion

This study concludes that the utterance of vowel “a” recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis.

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.

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2. Authors

In this paper, we propose a novel evolving clustering algorithm for streaming data entitled EdgeCluster. The proposed algorithm is resource efficient, making it suitable for use at edge devices with limited storage and computational capacity. The EdgeCluster is capable of modeling and monitoring a streaming data phenomenon and identifying outlying behavior. In parallel with the monitoring, the EdgeCluster algorithm dynamically maintains the set of clusters that models the phenomenon's normal behavioral scenarios by taking newly arrived data into account and updating the clustering model accordingly. The EdgeCluster algorithm is evaluated and benchmarked to another resource-Aware stream clustering algorithm, EvolveCluster, in two experimental data scenarios using synthetic and real-world datasets. © 2024 IEEE.

Generating precise saliency maps from eye tracker fixation points is a challenging task influenced by environmen-tal factors and the choice of evaluation metrics. This paper presents a novel, sustainable, scale-invariant, and sampling-independent method for converting fixation points into saliency maps. Leveraging the inherent predictability of human behavior, the proposed method ensures the highest compatibility with the chosen evaluation metric. Moreover, it introduces a mechanism to calculate the maximum achievable similarity score for each conversion. In addition, it offers crucial insights for both saliency map evaluation and the training of machine learning systems dedicated to saliency map generation. Experimental results demonstrate the method's efficacy in producing saliency maps that align seamlessly with diverse evaluation metrics, showcasing its adaptability and predictive capabilities. This approach con-tributes not only to the refinement of saliency map generation but also to the broader understanding of the intricacies involved in converting eye tracker data into meaningful ground truths. © 2024 IEEE.