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18th International Working Conference on Variability Modelling of software-Intensive Systems, VaMoS 2024

作者： Klikovits, Stefan Gambi, Alessio Dhungana, Deepak Rabiser, Rick Institute for Business Informatics Software Engineering Johannes Kepler University Linz Austria Institute for Digitalization Imc University of Applied Sciences Krems Austria Cdl VaSiCS Lit Cps Lab Johannes Kepler University Linz Austria

ISBN: (纸本)9798400708770

Extensive testing of Automated Driving Systems (ADS), such as Advanced Driver Assistance Systems and Autonomous Vehicles, is commonly conducted using simulators programmed to implement various driving scenarios, a technique known as scenario-based testing. ADS scenario-based testing using simulations is challenging because it requires identifying scenarios that can effectively test ADS functionalities while ensuring that driving simulators' features match the driving scenarios' requirements. This short paper discusses the main challenges of systematically conducting simulation-based testing and proposes leveraging software Product Line techniques to address them. Specifically, we argue that variability models can be used to support testers in generating test scenarios by effectively capturing and relating the variability in driving simulators, testing scenarios, and ADS implementations. We conclude by outlining an agenda for future research in this important area. © 2024 Owner/Author.

关键词： Autonomous vehicles

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UML and NFR-framework based method for the analysis of the requirements of an information system

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International Journal of Information Technology (Singapore) 2023年第1期15卷 411-422页

作者： Arif, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Computer Science and Technology Research Group Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Lab Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

software requirements are the expectation of stakeholders which are identified and modeled by various requirements elicitation and modeling techniques like traditional methods, goal oriented methods, and unified modeling language (UML), etc. The identified software requirements are broadly classified into functional requirements (FRs) and non-functional requirements (NFRs). Based on our literature review, we found that most of the focus in the area of software engineering is on the analysis of FRs as compared to NFRs. For the successful development of any information system both FRs and NFRs should be considered equally during the elicitation and modeling process;and ignoring NFRs may lead to failure of software. Therefore, to address this issue, we developed a method for the analysis of both FRs and NFRs so that a successful software product can be developed by a software company. In the proposed method, FRs are modeled by UML use-case diagram, class-diagram, and activity diagram. The NFRs are analyzed using NFR-framework;and NFR propagation rules have been derived from the requirements of an information system. In this framework, fuzzy based approach has been used to analyze the contribution links of softgoal interdependency graph. The applicability of the proposed method is discussed by using FRs and NFRs of library information system. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Functional requirements Library information system Non-functional requirements software requirements UML

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LoRaWAN in a rural context: Use cases and opportunities for agricultural businesses 24

LoRaWAN in a rural context: Use cases and opportunities for ...

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24th ITG-Symposium on Mobile Communication - Technologies and Applications

作者： Grunwald, Alexander Schaarschmidt, Marco Westerkamp, Clemens Lab for Software Engineering University of Applied Sciences Osnabrück Germany

ISBN: (纸本)9783800749614

Due to the lack of mobile infrastructure in rural areas, a lot of modern technologies can't be used efficiently. With Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN), there are new concepts for wireless long-range communication which have been established. They enable a modern technical solution to communicate in rural areas, where current mobile network coverage is missing. This paper investigates LoRaWAN for agriculture-based use cases. Hence, two use cases were evaluated. In the first use case, the temperature of a horse stable was measured and transmitted by taking the minimal use of the radio channel into account. In the second use case, a self-developed device was buried into the agriculture land at a depth of 10 down to 60 cm to analyze the soil properties and test the permeability of agriculture land. Additionally, a server and gateway architecture with access to a cloud system for data processing purposes was designed and in a second step, a low power prototype with different sensors for data collection for the described use cases was developed. The main benefit of this paper is the evaluation of LoRaWAN for the use in indoor and outdoor applications for agricultural businesses. The presented results are the first step for area-wide real-time monitoring of important agriculture data in rural areas which enables the precision reaction to physical changes. © VDE VERLAG GMBH Â Berlin Â Offenbach

关键词： Internet of things

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Masum, Mohammad Anjum, Nafisa Hossain Faruk, Md Jobair Shahriar, Hossain Sakib, Nazmus Valero, Maria Karim, M.A. Rahman, Akond Wu, Fan Cuzzocrea, Alfredo San Jose State University Department of Applied Data Science San Jose United States Kennesaw State University Department of Information Technology United States Kennesaw State University Department of Software Engineering and Game Development United States Kennesaw State University Department of Civil and Environmental Engineering United States Auburn University Department of Computer Science and Software Engineering United States Tuskegee University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9781665480451

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score. © 2022 IEEE.

关键词： Forecasting

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COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

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CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images

arXiv

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arXiv 2023年

作者： Ali, Momina Liaqat Rauf, Zunaira Khan, Asifullah Sohail, Anabia Ullah, Rafi Gwak, Jeonghwan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Computer Science Faculty of Computing and Artificial Intelligence Air University IslamabadE-9 Pakistan Perak31750 Malaysia Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

Transformers, due to their ability to learn long-range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. However, their multi-head attention module only captures global-level feature representations, which is insufficient for medical images. To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images. The proposed CB-HVT comprises five modules to effectively identify lymphocytes. Its (i) channel generation module, uses the idea of channel boosting through transfer learning to extract diverse channels from different auxiliary learners. These boosted channels are first concatenated and ranked using an attention mechanism in the (ii) channel exploitation module. A fusion block is then utilized in the (iii) channel merging module for a gradual and systematic merging of the diverse boosted channels to improve the network’s learning representations. The CB-HVT also employs a proposal network in its (iv) region aware module followed by its (v) detection and segmentation head to accurately identify and distinguish objects, even in the regions with crowded presence and artifacts. We evaluated the proposed CB-HVT on two publicly available datasets for lymphocyte assessment in histopathological images. The results demonstrate that CB-HVT has a good generalization on unseen data therefore, it can serve as a valuable tool for pathologists. Copyright © 2023, The Authors. All rights reserved.

关键词： Lymphocytes

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Automated File labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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A Recent Survey of Vision Transformers for Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Khan, Asifullah Rauf, Zunaira Khan, Abdul Rehman Rathore, Saima Khan, Saddam Hussain Shah, Sahar Farooq, Umair Asif, Hifsa Asif, Aqsa Zahoora, Umme Khalil, Rafi Ullah Qamar, Suleman Asif, Umme Hani Khan, Faiza Babar Majid, Abdul Gwak, Jeonghwan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Avid Radio pharmaceuticals PhiladelphiaPA United States Eli Lilly and Company IndianapolisIN United States Swat Pakistan Air University E-9 Islamabad44230 Pakistan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

Medical image segmentation plays a crucial role in various healthcare applications, enabling accurate diagnosis, treatment planning, and disease monitoring. In recent years, Vision Transformers (ViTs) have emerged as a promising technique for addressing the challenges in medical image segmentation. In medical images, structures are usually highly interconnected and globally distributed. ViTs utilize their multi-scale attention mechanism to model the long-range relationships in the images. However, they do lack image-related inductive bias and translational invariance, potentially impacting their performance. Recently, researchers have come up with various ViT-based approaches that incorporate CNNs in their architectures, known as Hybrid Vision Transformers (HVTs) to capture local correlation in addition to the globa l information in the images. This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation. Along with the categorization of ViT and HVT-based medical image segmentation approaches we also present a detailed overview of their real-time applications in several medical image modalities. This survey may serve as a valuable resource for researchers, healthcare practitioners, and students in understanding the state-of-the-art approaches for ViT-based medical image segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Sakib, Nazmus Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480451

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2022 IEEE.

关键词： Supply chains

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Optimal Resource Utilization in Hyperledger Fabric: A Comprehensive SPN-Based Performance Evaluation Paradigm

Optimal Resource Utilization in Hyperledger Fabric: A Compre...

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IEEE Symposium on Network Operations and Management

作者： Carlos Melo Glauber Gonçalves Francisco A. Silva Leonel Feitosa Iure Fé André Soares Eunmi Choi Tuan Anh Nguyen Dugki Min Applied Research on Distributed Systems Lab (PASID) Federal University of Piauí (UFPI) Brazil School of Software College of Computer Science Kookmin University Seoul South Korea Konkuk Aerospace Design-Airworlhiness Research Inslilule (KADA) Konkuk Universily Seoul Soulh Korea Department of Computer Science and Engineering Konkuk University Seoul South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

关键词： Performance evaluation Distributed ledger Computational modeling Data security Petri nets Stochastic processes Fabrics

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