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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Hüseyin Ünlü Samet Tenekeci Can Çiftçi İbrahim Baran Oral Tunahan Atalay Tuna Hacaloğlu Burcu Musaoğlu Onur DemirǶrs Department of Computer Engineering Izmir Institute of Technology Izmir Turkiye Bilgi Grubu Izmir Turkiye Department of Information Systems Engineering Atilim University Ankara Turkiye Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Siskon Software & Automation Izmir Turkiye

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

software Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.

关键词： Training Schedules Accuracy Estimation Project management Companies Predictive models Size measurement software Natural language processing

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Sensor data modeling with Bayesian networks

Sensor data modeling with Bayesian networks

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2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2022

作者： Silva, Carla Rodrigues, Arlete Jorge, Alipio Dutra, Ines Bosch Security Systems - Sistemas de Segurança S.A. FIR Software Engineering Ovar Ovar Portugal University of Porto LIAAD Department of Computer Science Porto Portugal University of Porto CINTESIS Department of Computer Science Porto Portugal

ISBN: (数字)9781665451260

ISBN: (纸本)9781665451260

This research aims to extract knowledge of sensors behavior resorting to Bayesian networks (BNs) and dynamic Bayesian networks (DBNs), a time-based BN version. These two types of models belong to the group of probabilistic graphical models (PGMs). These graphical models can be very useful to get insights from data in order to improve sensor capabilities in the industry of fire detection systems, since it can provide the conditional dependence structure among various sensor variables. Relevant sensors with fire alerts were selected and studied at device level. We conduct a data fusion analysis since we deal with heterogeneous data sources, Remote Alert (RA) with sensor states and Condition Monitoring (CM) with numerical data. To achieve an accurate fusion of the data, a pipeline was designed to align both sources of data in a regular time interval. Furthermore, a change point detection (CPD) method was used to discretize the numerical variables. In addition, one-hot encoding was used to create binarized datasets and combine all data (RA+CM). Our modeling helps understanding the dependencies among the sensor variables, highlighting that individual devices of the same type can have a very different probabilistic behavior along the time, probably due to be installed in distinct regions. Moreover, the models helped capturing strange probabilistic sensor behavior such as a low probability of a NORMAL state happening given that states FIRE, WARNING and TROUBLE did not happen. © 2022 IEEE.

关键词： Bayesian networks

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research

A Review of Multi-omics and Machine Learning: Advancements a...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hazem Ayman Madonna Bassem Malak Madkour Mariam Fawzi Nada Amr Youmna Yousry Ziad Elgayar Ashraf Abdelraouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem.

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Supporting the Episodic Application of Clinical Guidelines over Significant Time Periods

Supporting the Episodic Application of Clinical Guidelines o...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Ben Shahar, Bruria Shahar, Yuval Jaffe, Shai Cohen, Odeya Shalom, Erez Selivanova, Maya Rimon, Ephraim Goldstein, Ayelet Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Nursing Faculty of Health Sciences Ben-Gurion University Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University Israel Herzfeld Geriatric Rehabilitation Medical Center Clalit Health Services Israel Department of Computer Science Hadassah Academic College Jerusalem Israel

ISBN: (纸本)9781643685335

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations. © 2024 The Authors.

关键词： Random errors

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care

Using Formal Knowledge to Support Episodic Evidence-Based Nu...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Jaffe, Shai Ben Shahar, Bruria Shahar, Yuval Goldstein, Ayelet Shalom, Erez Selivanova, Maya Rimon, Ephraim Cohen, Odeya Department of Nursing Faculty of Health Sciences Ben-Gurion University of the Negev Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Computer Science Department Hadassah Academic College Israel Herzfeld Geriatric Rehabilitation Medical Center Israel

ISBN: (纸本)9781643685335

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge. © 2024 The Authors.

关键词： Nursing

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study

UVM Based Verification Framework for Deep Learning Hardware ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Randa Aboudeif Tasneem A. Awaad Mohamed AbdElsalam Yehea Ismail Electronics and Communications Engineering Department The American University in Cairo Siemens Digital Industries Software Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Siemens Digital Industries Software Ain Shams University Cairo Egypt Siemens Digital Industries Software Cairo Egypt Electronics and Communications Engineering Department The American University in Cairo Cairo Egypt

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study.

关键词： Deep learning Runtime Design methodology Emulation Stability analysis Convolutional neural networks Reliability Hardware acceleration Field programmable gate arrays Testing

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Structural Reliability of a Multipath Routing Network with Reconfigurations when Switching Routes

Structural Reliability of a Multipath Routing Network with R...

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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Bogatyrev, Vladimir Anatolievich Le, Anh Tu Abramova, Evgenia Alexandrovna Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia University Itmo Faculty of Software Engineering and Computer Technology Saint Petersburg Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The reliability of data transmission is one of the most important factors in building network models. The need for timely delivery of packets to the user leads to the search for new methods to improve the configuration of network structures. A study of the possibility to improve the structural reliability of multipath routing networks in the presence of request sources with full and non-full connection to a set of network paths has been conducted. Improving the reliability and fault tolerance of multipath routing networks is based on a configuration that allows new routes to be formed as a result of using segments of existing paths that have remained connected after failures. The investigated possibility of increasing the reliability of a multipath network is achieved by optimizing the placement of route switch nodes. © 2022 IEEE.

关键词： Network routing

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A Smart Assistive Device for the Blind People 7th

A Smart Assistive Device for the Blind People

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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Chellatamilan, T. Santhi, K. Reddy, R. Hemadri Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789811996375

Mobility is the biggest issue for blind individuals. In order to move around, they must rely on others. Therefore, we are creating a navigational aid for blind people, to help the blind people in the world from various difficulties they face in day-to-day life. The World Health Organization (WHO) reported that a survey was conducted in 2014 and that there are an estimated 285 million visually impaired persons worldwide, of which 37 million are blind, with over 15 million of them being Indian. The majority of blind persons depend on other people for their activities since INDIA accounts for 21% of the global population of blind people (Khanam et al. in Int J Adv Res Sci Eng 7:195–199, 2018). Smart shoes play an important role. Obstacle detection is performed using ultrasonic sensor which is also done by many people in the previous projects or researches. The new concept that is included in this paper is that it can also detect any flame and potholes around the person using flame sensor and ultrasonic sensor, respectively. And we use shock sensor to detect any shock that occurs. If the smart assistive device or smart shoes senses an object, a pothole, a fire (from about three feet away), or a shock in front of the user, a buzzer will sound. These sensors are integrated using Arduino Uno. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle detectors

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