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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Daud Haiderzai, Mohammad Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

software development demand and identification of end users involvement have been increasing rapidly, but identifying the real end user and involving them in software development is challenging for software developers reported in the literature. One of the problems is the difficulty of designing the business domain. It takes longer to find the actual business domain when different and inexpert domain end users are engaged with developers. Another problem is the difficulty of incorporating all types of end users needs in software development. Several expectations and different information make the development process more complex and overload the project scope. software development also takes more time, and the project goal is challenging. The above problems are the leading causes that create a complex process and difficulty in software development. To overcome these problems, we established a pattern language composed of eight organizational patterns observed and documented during in practical work on many projects. These patterns and other known organizational patterns relation are described in a patterns story of a real company. © 2022 ACM.

关键词： software design

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Organizational Patterns for Improving the Understanding between a Customer and the Development Team 27

Organizational Patterns for Improving the Understanding betw...

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Khattab, Mohammad Ismail Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

During a software development process, a customer and the development team need to communicate and understand each other. Poor communication between a customer and the development team is one of the most common challenges in the software development process, which leads to software failures and further increases the budget. In our practical experience and from the literature, we found that lack of customer involvement, no common communication language, complex business processes, end-user resistance toward system development, lack of trust, lack of leadership, and and end-user capacity are the main challenges toward the development team understanding. We bring six newly discovered patterns that address these problems and improve the understanding between a customer and the development team during the software development process. We also bring some of the known organizational patterns that also mitigate these problems into relation to the newly discovered patterns. To create a pattern language from these patterns, we illustrated the sequences of the patterns so that the development team and customer could understand each other. © 2022 ACM.

关键词： software design

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Real-Time Masked Face Recognition Using FaceNet and Supervised Machine Learning 1

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2nd International Conference on Electronics, Biomedical engineering, and Health informatics, ICEBEHI 2021

作者： Adhinata, Faisal Dharma Tanjung, Nia Annisa Ferani Widayat, Widi Pasfica, Gracia Rizka Satura, Fadlan Raka Department of Software Engineering Faculty of Informatics Institut Teknologi Telkom Purwokerto Purwokerto Indonesia Department of Informatics Engineering Faculty of Informatics Institut Teknologi Telkom Purwokerto Purwokerto Indonesia

ISBN: (数字)9789811918049

ISBN: (纸本)9789811918032

The coronavirus pandemic has led to the implementation of health protocols such as the use of masks worldwide. Without exception, work activities also require the wearing of masks. This condition makes it difficult to recognize an individual's identity because the mask covers half of the face, especially when the employee is present. The attendance system recognizes a face without a mask more accurately, in contrast, a masked face makes identity recognition inaccurate. Therefore, this study proposes a combination of facial feature extraction using FaceNet and several classification methods. The three supervised machine learning methods were evaluated, namely multiclass Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest. Furthermore, the masked face recognition system was evaluated using real-time video data to assess the accuracy and processing time of the video frame. The accuracy result on real-time video data using a combination of FaceNet with K-NN, multiclass SVM, or Random Forest of 96.03%, 96.15%, and 54.04% are obtained respectively and in processing time per frame of 0.056 s, 0.055 s, and 0.061 are obtained respectively. The results show that the combination of the FaceNet feature extraction method with multiclass SVM produces the best accuracy and data processing speed. In other words, this combination can reach 18 fps at real-time video processing. Based on these results, the proposed combined method is suitable for real-time masked face recognition. This study provides an overview of the masked face recognition method so that it can be a reference for the contactless attendance system in this pandemic era. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Comparison of Visual Occupancy Detection Approaches for Parking Lots and Dedicated Containerized REST-API Server Application 9

Comparison of Visual Occupancy Detection Approaches for Park...

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9th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2022

作者： Petričko, Andrej Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

With the development of new technology, the urban infrastructure, which is necessary to meet the social, economic, and physical needs of the population, is also gradually improving. Therefore, cities face significant urban planning challenges. With the increasing population and number of cars on the road parking space occupancy detection techniques are of great importance for efficient parking space management. Knowing the availability of parking spaces within a city in real-time and informing residents can be of great help in reducing the number of car park councils, reducing traffic congestion in cities, or improving the scalability of car parks and the time needed to find empty parking space. In many car parks, ground sensors are used to determine occupancy status. However, this requires the installation and maintenance of these sensors in every parking space. This can be expensive, especially in car parks with a large number of available spaces. With increasing computing power, new approaches to parking space occupancy detection using computer vision are emerging. However, these approaches are tailored to the site of application. In this work, we propose a comparison of two general approaches: classification and object detection. The reliability, precision, and scalability of these approaches are compared using a REST-API server application implemented in Python. In order to get as close as possible to general use, we use docker, which enables us easier develop and deploy this application independently of the hardware. © 2022 Copyright for this paper by its authors.

关键词： Object detection

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Toward Organizational Pattern Ontology 27

Toward Organizational Pattern Ontology

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Waseeb, Shakirullah Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

Organizational patterns of agile software development are proven practices for dealing organizational principles. Finding and selecting the right pattern is difficult. One way to select a pattern is to follow the sequence and compositions given in a pattern language. However, in general, pattern languages do not always reveal every reliable connection. Patterns are described in informal and unstructured text, making it difficult to understand their connections. This work attempts to provide a conceptual ontology model for organizational patterns-describing the collection of concepts (terms) and their relations. This contribution can be an attempt to express, expose, and share semantic knowledge between patterns using ontology. The resulting ontology can be used on top of the organizational patterns repositories to help retrieve patterns based on their logical connections. © 2022 ACM.

关键词： Semantics

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End-to-end entity extraction from OCRed texts using summarization models

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Neural Computing and Applications 2024年第35期36卷 22347-22363页

作者： Villa-García, Pedro A. Alonso-Calvo, Raúl García-Remesal, Miguel Biomedical Informatics Group Department of Artificial Intelligence Universidad Politécnica de Madrid Campus Montegancedo s/n Boadilla del Monte Madrid28660 Spain Biomedical Informatics Group Department of Languages Informatic Systems and Software Engineering Universidad Politécnica de Madrid Campus Montegancedo s/n Boadilla del Monte Madrid28660 Spain

A novel methodology is introduced for extracting entities from noisy scanned documents by using end-to-end data and reformulating the entity extraction task as a text summarization problem. This approach offers two significant advantages over traditional entity extraction methods while maintaining comparable performance. First, it utilizes preexisting data to construct datasets, thereby eliminating the need for labor-intensive annotation procedures. Second, it employs multitask learning, enabling the training of a model via a single dataset. To evaluate our approach against state-of-the-art methods, we adapted three commonly used datasets, namely, Conference on Natural Language Learning (CoNLL++), few-shot named entity recognition (Few-NERD), and WikiNEuRal domain adaptation (WikiNEuRal + DA), to the format required by our methodology. We subsequently fine-tuned four sequence-to-sequence models: text-to-text transfer transformer (T5), fine-tuned language net T5 (FLAN-T5), bidirectional autoregressive transformer (BART), and pretraining with extracted gap sentences for abstractive summarization sequence-to-sequence models (PEGASUS). The results indicate that, in the absence of optical character recognition (OCR) noise, the BART model performs comparably to state-of-the-art methods. Furthermore, the performance degradation was limited to 3.49–5.23% when 39–62% of the sentences contained OCR noise. This performance is significantly superior to that of previous studies, which reported a 10–20% decrease in the F1 score with texts that had a 20% OCR error rate. Our experimental results demonstrate that a single model trained via our methodology can reliably extract entities from noisy OCRed texts, unlike existing state-of-the-art approaches, which require separate models for correcting OCR errors and extracting entities. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optical character recognition

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Utilizing Organizational Patterns to Build High-Performing Remote Teams 28

Utilizing Organizational Patterns to Build High-Performing R...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Toldy, Kristián Khail, Waheedullah Sulaiman Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer programming

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Classification of Cervical Cell Images into Healthy or Cancer Using Convolution Neural Network and Linear Discriminant Analysis

Classification of Cervical Cell Images into Healthy or Cance...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Sholik, Mohammad Fatichah, Chastine Amaliah, Bilqis Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Telkom Surabaya Department of Software Engineering Surabaya Indonesia

ISBN: (纸本)9798350313635

Cancer of the cervix is the disease that accounts for the majority of deaths in women. This disease accounts for nearly 12% of all cancers and has a high risk of death for women worldwide. If precancerous lesions are found early, the disease can be cured. Pap smear screening is known for its reliability and effectiveness in detecting cervical cell abnormalities early, but there is a risk of errors in manual image analysis. Using deep learning approaches in the domains of medicine and healthcare can be used for decision support systems to remove bias from observations. This paper presents a framework that utilizes deep learning and techniques to reduce the dimensions of features. The suggested framework captures deep features from a convolutional neural network (CNN) model and employs a feature reduction approach using linear discriminant analysis (LDA) to ensure computational cost reduction. The feature dimension derived from the CNN model produces a huge feature space that requires a feature reduction to eliminate redundant features. The features that have been reduced by linear discriminant analysis are used for the training of three classifiers, namely SVM, MLP, and K-NN, to generate final predictions. The evaluation of the proposed framework involved the utilization of three datasets that are openly accessible: the Herlev dataset, the Mendeley dataset, and the SIPaKMeD dataset, which achieved classification accuracies of 95.65% (SVM and MLP), 100% (MLP), and 97.54 (K-NN), respectively. © 2023 IEEE.

关键词： Diseases

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： Computer vision

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Identification of software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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Computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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