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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Sonja M. Hyrynsalmi Helena Holmström Olsson Jan Bosch Dept. Software Engineering LUT University Lahti Finland Dept. Computer Science and Media Technology Malmö University Malmö Sweden Dept. Computer Science and Engineering Chalmers University of Technology Göteborg Sweden

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management.

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Towards a Data Business Maturity Model for software-intensive Embedded System Companies

Towards a Data Business Maturity Model for Software-intensiv...

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IEEE International Conference on engineering, Technology and Innovation

作者： Sami Hyrynsalmi Helena Holmström Olsson Jan Bosch Dept. Software Engineering LUT University Lahti Finland Dept. Computer Science and Media Technology Malmö University Malmö Sweden Dept. Computer Science and Engineering Chalmers University of Technology Göteborg Sweden

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and suﬃcient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive *** multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses.

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network

arXiv

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arXiv 2024年

作者： Go, Young-Min Yu, Seong-Hyun Park, Hyeong-Yeong Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Chungbuk28644 Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Gyeonggi14662 Korea Republic of

Brain-computer interface (BCI) technology facilitates communication between the human brain and computers, primarily utilizing electroencephalography (EEG) signals to discern human intentions. Although EEG-based BCI systems have been developed for paralysis individuals, ongoing studies explore systems for speech imagery and motor imagery (MI). This study introduces FingerNet, a specialized network for fine MI classification, departing from conventional gross MI studies. The proposed FingerNet could extract spatial and temporal features from EEG signals, improving classification accuracy within the same hand. The experimental results demonstrated that performance showed significantly higher accuracy in classifying five finger-tapping tasks, encompassing thumb, index, middle, ring, and little finger movements. FingerNet demonstrated dominant performance compared to the conventional baseline models, EEGNet and DeepConvNet. The average accuracy for FingerNet was 0.3049, whereas EEGNet and DeepConvNet exhibited lower accuracies of 0.2196 and 0.2533, respectively. Statistical validation also demonstrates the predominance of FingerNet over baseline networks. For biased predictions, particularly for thumb and index classes, we led to the implementation of weighted cross-entropy and also adapted the weighted cross-entropy, a method conventionally employed to mitigate class imbalance. The proposed FingerNet involves optimizing network structure, improving performance, and exploring applications beyond fine MI. Moreover, the weighted Cross Entropy approach employed to address such biased predictions appears to have broader applicability and relevance across various domains involving multi-class classification tasks. We believe that effective execution of motor imagery can be achieved not only for fine MI, but also for local muscle MI. © 2024, CC BY-NC-ND.

关键词： Electroencephalography

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Smart environmental solution for modern urbanization using mobile application. 3

Smart environmental solution for modern urbanization using m...

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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Fahim, Wasik Ahmmed Sarkar, Puza Rani Tonmoy, Shakib Salim Pranto, Atiqur Rahman Islam, Md. Ashiqul Khatun, Tania Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9780738111834

Nowadays modern civilization having a huge development in technology but we do not get obviate pollution due to the huge amount of population. Behind this, the most of the problem is continuously throwing the garbage's outside and have become stuck up anywhere. There are tons of applications supported the environment and pollution but don't have any effective solution. This paper attempts to develop a flutter based Mobile Application with Google maps API to help the users to reduce the pollution in their locality. User can mark the locations of garbage and can also mail or call the closest authority. In order that the authority can clean or take necessary steps counting on the user's report. For increasing people's awareness, it shows real-time air quality and pollution level supported on web scrapping data from different websites and IoT based systems. During this modern technology era, mobile applications are the foremost popular. So this application may be a very effective to reduce pollution by public awareness or by making communication between user and the authority. © 2021 IEEE.

关键词： Internet of things

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A Novel Dual FedAux-based Harvested Energy Prediction for Road Safety Systems

A Novel Dual FedAux-based Harvested Energy Prediction for Ro...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Papa Sidy Mactar Traoré Wael Jaafar Fatoumata Baldé Dept. Computer Science Université Alioune Diop Bambey Sénégal Dept. Software and IT Engineering École de Technologie Supérieure (ÉTS) Montreal Canada

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

In the evolving landscape of road safety, the role of solar-powered Internet-of-Things (IoT) devices, crucial for supporting connected and autonomous vehicles (CAVs) with enhanced computing and storage capabilities, is increasingly significant These devices, often deployed in remote areas such as transcontinental highways, rely heavily on their energy harvesting capabilities for optimal operation. Given their expected widespread deployment, this paper explores the challenge of accurately predicting harvested energy in such IoT devices. To do so, we introduce "Dual FedAux", a novel approach that blends Federated Learning with a dual-model architecture, integrating long short-term memory (LSTM) and Transformer models. This combination, which is enhanced with meta-learning, i.e., stacking ensemble learning, is particularly adapted for time series analysis, such as for energy prediction. Through experiments, we demonstrate that the proposed Dual FedAux significantly outperforms benchmark models in terms of root-mean-square error (RMSE) and convergence, thus underscoring its potential to enhance the efficiency and reliability of large-scale road safety IoT-based systems.

关键词： Computational modeling Time series analysis Solar energy computer architecture Predictive models Transformers Road safety

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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Dual-Modality Deep Feature-based Anomaly Detection for Video Surveillance

Dual-Modality Deep Feature-based Anomaly Detection for Video...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Parth Lalitkumar Bhatt Dhruva Shah Christopher Silver Wandong Zhang Thangarajah Akilan Dept. of Computer Science Lakehead University Thunder Bay Canada Dept. of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Dept. of Electrical and Computer Enggineering Western University London Canada Dept. of Software Engineering Lakehead University Thunder Bay Canada

Detecting anomalies in videos is not only crucial but also an intriguing task in surveillance systems. It is a sequential modeling problem in nature that requires careful selection of spatial and temporal dependent patterns from a sequence of frames. There are several research works from traditional approaches to modern deep learning-based techniques introduced to address this problem. However, there is a huge demand for research and development to ameliorate the performance of the existing solutions. In response to that, this study proposes an improved video anomaly detection model using deep features extracted from a dual-modality input representation. The proposed model demonstrates effectiveness in the benchmark–UCF crime dataset by achieving the best AUC of 87.52%, which is ≈ 12.3% improvement compared to a baseline. The application aspect of this work includes strengthening the security measures in common places, viz. airports, banks, public transits, schools, and shopping complexes by detecting aberrational or suspicious activities in surveillance videos.

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Rare and Common Types of Retinal Disease Recognition Using Ensemble Deep Learning

Rare and Common Types of Retinal Disease Recognition Using E...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Al Shahriar Uddin Khondakar Pranta Arafath Bin Mohiuddin Amira Hossain Anamul Sakib Md. Moniruzzaman Shanto Shamim Forhad Abdullah Al-Sakib Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Computer Science Wright State University Dayton USA Department of Collage of Engineering and Technology Westcliff University CA USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Detecting retinal diseases is crucial for preventing vision loss, but it comes with challenges, such as data imbalance and accurately classifying rare conditions. This study introduces a new ensemble transfer learning model that combines EfficientNetB7, SE-ResNet, DenseNet-169, and NASNet to improve the classification of common and rare retinal diseases. The ensemble uses a probability averaging approach to maximise each architecture’s strengths. Essential preprocessing techniques such as Global Contrast Normalization (GCN) and Total Variation Denoising (TVD) were used to enhance image quality. The model was trained and evaluated on the Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0, achieving 98.65% accuracy and an F1-score of 0.96% for common diseases and 99.10% accuracy with an F1-score of 0.97 for rare diseases, outperforming individual models significantly. The ensemble has been integrated into a web-based application that allows clinicians to upload retinal images for instant diagnostic feedback, enabling real-time analysis and visual insights. Despite its strengths, challenges such as high computational demands and reliance on high-resolution photos remain. Future work will improve computational efficiency through model compression techniques and extend the model to other imaging modalities, such as Optical Coherence Tomography (OCT). In conclusion, this study presents a robust and scalable solution for retinal disease classification, potentially improving early detection and providing real-time support in clinical settings, particularly in resource-limited environments.

关键词： Deep learning Visualization Accuracy Computational modeling Optical coherence tomography Transfer learning Imaging Retina Real-time systems Diseases

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Characterization and Control of a Superconducting Homopolar Motor with Cryogenic GaN Inverter for Electric Aircrafts

Characterization and Control of a Superconducting Homopolar ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Gliese, Felix Pearce, Matthew G. S. Wadsworth, Aaron Siamaki, Mohammad Storey, James Lumsden, Grant Thrimawithana, Duleepa J. Badcock, Rod Roth-Stielow, Joerg Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand OpenStar Technologies New Zealand Victoria University of Wellington Robinson Research Institute New Zealand University of Stuttgart Institute for Power Electronics and Electrical Drives Germany

ISBN: (纸本)9798350376067

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor's magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights. © 2024 IEEE.

关键词： Gallium nitride

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