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arXiv 2025年

作者： Tayfor, Noor B. Rashid, Tarik A. Qader, Shko M. Hassan, Bryar A. Abdalla, Mohammed H. Majidpour, Jafar Ahmed, Aram M. Ali, Hussein M. Aladdin, Aso M. Abdullah, Abdulhady A. Shamsaldin, Ahmed S. Sidqi, Haval M. Salih, Abdulrahman Yaseen, Zaher M. Ameen, Azad A. Nayak, Janmenjoy Hamza, Mahmood Yashar Department of Information Technology Kurdistan Technical Institute Sulaimani Iraq Computer Science and Engineering Department University of Kurdistan Hewlêr Erbil Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq Department of Computer Science University of Raparin Rania Iraq Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Department of Software Engineering College of Engineering and Computational Science Charmo University Chamchamal46023 Iraq Department of Database College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Northumbria University London United Kingdom Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhah-ran31261 Saudi Arabia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada India Department of Computer Engineering Tishk International University Erbil Iraq

The widespread availability of video recording through smartphones and digital devices has made video-based evidence more accessible than ever. Surveillance footage plays a crucial role in security, law enforcement, and judicial processes. However, with the rise of advanced video editing tools, tampering with digital recordings has become increasingly easy, raising concerns about their authenticity. Ensuring the integrity of surveillance videos is essential, as manipulated footage can lead to misinformation and undermine judicial decisions. This paper provides a comprehensive review of existing forensic techniques used to detect video forgery, focusing on their effectiveness in verifying the authenticity of surveillance recordings. Various methods, including compression-based analysis, frame duplication detection, and machine learning-based approaches, are explored. The findings highlight the growing necessity for more robust forensic techniques to counteract evolving forgery methods. Strengthening video forensic capabilities will ensure that surveillance recordings remain credible and admissible as legal evidence. © 2025, CC BY.

关键词： CCD cameras

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Building an agent for factual question generation task 1

Building an agent for factual question generation task

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1st IEEE World Symposium on Digital Intelligence for Systems and Machines, DISA 2018

作者： Blstak, Miroslav Rozinajova, Viera Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Institute of Informatics Ilkovicova 2 Bratislava Slovakia

ISBN: (纸本)9781538651025

With the boom of e-learning and online education systems, also question generation systems have become more interesting. Nowadays, it is relatively common and simple to use internet and web technologies for learning. There are many sources of educational materials in various formats like text, audio, video or interactive applications and games. But the automatic evaluation and confirmation of students' knowledge is still difficult. In this paper, we describe our endeavour to design and create an interactive educational agent which to some extent acts as a teacher: it automatically generates factual questions from the educational text and tries to reveal if the student understood the information presented there. We have evaluated our agent in comparison with state-of-the-art question generation systems. In order to assess the applicability of generated questions in educational process we have also compared these questions with those expected by the students. © 2018 IEEE.

关键词： Students

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Fusion of Super-Resolution and Semantic Segmentation Deep Models for Building Footprint Extraction From Aerial Images

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The Photogrammetric Record 2025年第190期40卷

作者： Ziyi Chen Yuhua Luo Jiaying Zhang Ronggang Guo Liai Deng Jinghua Liu Dilong Li Yongtao Yu Ammar Abulibdeh Cheng Wang Department of Computer Science and Technology Fujian Key Laboratory of big Data Intelligence and Security Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen FJ China Guojiao Spatial Information Technology (Beijing) co. Ltd Beijing China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian JS China Department of Humanities College of Arts and Sciences Qatar University Doha Qatar School of Informatics Xiamen University Xiamen FJ China

The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. Thus, this paper introduces a novel approach that combines super-resolution reconstruction with semantic segmentation deep models for building footprint extraction. To begin, a super-resolution reconstruction deep model is employed to enhance the resolution of the target images. Subsequently, training samples are generated using various data augmentation techniques based on the reconstructed images. Finally, a semantic segmentation deep model is trained to perform the final building footprint extraction. For validation, our proposed framework was tested on two publicly available aerial image datasets: the WHU building dataset and the Massachusetts building dataset. The results obtained from these datasets demonstrated the effectiveness of our method, achieving high Intersection over Union (IoU) scores of 90.57% and 75.02%, respectively. Furthermore, our approach outperformed seven other competing methods, showcasing its superior performance.

关键词： aerial image building footprint extraction deep learning semantic segmentation super-resolution

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Multi-sector assessment framework – a new approach to analyse cybersecurity challenges and opportunities 1

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10th International Conference on Multimedia Communications, Services and Security, MCSS 2020

作者： Pappalardo, Salvatore Marco Niemiec, Marcin Bozhilova, Maya Stoianov, Nikolai Dziech, Andrzej Stiller, Burkhard CIRM Viale Zara 81 Milan Italy Software Engineering Italia S.r.l. Via Santa Sofia 64 Catania Italy University of Messina COSPECS PhD St via Concezione 8 Messina Italy AGH University of Science and Technology Mickiewicza 30 Krakow30-059 Poland Bulgarian Defence Institute Sofia Bulgaria Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH Binzmühlestrasse 14 Zurich8050 Switzerland

ISBN: (数字)9783030590000

ISBN: (纸本)9783030589998

This paper presents a new approach to analyse cybersecurity challenges and opportunities, focused on the development of a new risk assessment and management framework. Such a multi-sector assessment framework should be able to evaluate and prioritize cybersecurity risks in trans-sectoral and inter-sectoral contexts. It leads toward proper resource allocations and mitigation actions. To achieve this goal, the analysis of existing risk assessment and management frameworks was performed. Also, an overview on common multi-sectoral technological challenges and opportunities were provided being derived from sector-specific use cases as well as transversal and inter-sectoral challenges and opportunities. As a result of this analysis the architecture of the ECHO Multi-sector Assessment Framework was proposed. The identified technological challenges and opportunities, multi-sector dependencies, and transversal aspects determine the input data for the new framework. The architecture is applicable in healthcare, energy, maritime transportation, and defence sectors, being extensible to others. The framework enables the definition of governance models or the design of cybersecurity technology roadmaps. © Springer Nature Switzerland AG 2020.

关键词： Risk assessment

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Guidelines for the Search Strategy to Update Systematic Literature Reviews in software engineering

arXiv

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arXiv 2020年

作者： Wohlin, Claes Felizardo, Katia Romero Mendes, Emilia Kalinowski, Marcos Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Department of Computing Federal Technological University of Paraná Cornélio Procópio Brazil Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden Department of Informatics Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil

Context: Systematic Literature Reviews (SLRs) have been adopted within software engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. Copyright © 2020, The Authors. All rights reserved.

关键词： software engineering

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Novel approaches to evaluate the ability of vehicles for secured transportation

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Periodica Polytechnica Transportation engineering 2020年第1期49卷 80-88页

作者： Kocsis, Bence Vida, Gábor Szalay, Zsolt Ágoston, György Department of Automotive Technologies Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics P. O. B. 91 BudapestH-1521 Hungary Department of Software Development and Application Institute of Informatics University of Dunaújváros P. O. B. 152 DunaújvárosH-2401 Hungary

The assurance of process safety plays an important role in the field of information technology. Securing the information has become one of the biggest challenges in the present day. Whenever we think about the protected systems the first thing that comes to our mind can be malicious interventions which are increasing immensely day by day. Nowadays we live the world of huge automotive developments with the appearance of the demand for autonomous vehicles. On the other hand, technological developments also provide a lot of advantages for the society. The benefits of autonomous cars include reduced mobility costs, increased safety, increased mobility, significant reduction of traffic collisions. However, it cannot be forgotten that the extension of cyberspace affects the transportation increasingly. Accordingly, cars are produced with high level of connectivity and automation. Therefore, the risks arriving from the cyberspace can now endanger the safe and secured transportation. These tendencies shall motivate manufacturers and developers to permanently improve the ability of vehicles to protect themselves and their passengers. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Autonomous vehicles

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Evaluation of highway-pilot function based on FMEA safety analysis

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Periodica Polytechnica Transportation engineering 2020年第3期48卷 253-259页

作者： Bényei, Ádám Vida, Gabor Pintér, Krisztián Szalay, Zsolt Ágoston, György Department of Automotive Technologies Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics P. O. B. 91 BudapestH-1521 Hungary Department of Software Development and Application Institute of Informatics University of Dunaújváros Dunaújváros P. O. B. 152 H-2401 Hungary

Cybersecurity is becoming more and more relevant. Autonomous vehicles handle large amounts of data and can connect to more and more existing devices, smartphones, tablets, or even other cars and systems. This poses the risk of unauthorized access to data. Theoretically cars have separate computer units, operate in isolation, and are not connected, so there is less possibility to be attacked. However if the vehicles are interconnected, hackers can have easier access to personal data. They can get information about the location of the car owner, their typical trips, and, for example, allow an intruder to know when the tracked person is not at home. Furthermore it can also be happened that the vehicle operation is maliciously disturbed, which can result in a security risk for the passengers. In extreme cases, computer terrorist attacks can also be prepared - large-scale interventions on roads can lead to chaos across a region or country. In accordance with the introduced threats, it is a crucial objective of this research to indicate specific methods, which can help the industry to evaluate and prepare for these kinds of attacks in a proper way. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Vehicle safety

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Visualization of Variability Analysis of Control software From Industrial Automation Systems

Visualization of Variability Analysis of Control Software Fr...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Safa Bougouffa Birgit Vogel-Heuser Juliane Fischer Ina Schaefer Huaxia Li Institute of Automation and Information Systems Technical University of Munich Munich Germany Institute of Software Engineering and Automotive Informatics Technical University of Braunschweig Brauschweig Germany

Industrial automated production systems are mechatronic and long living systems that undergo changing requirements throughout their life cycle. While the proportion of functionality implemented by software is growing, adjustments are usually implemented using a clone-and-own principle, which results in unmanaged software variants and versions. Furthermore, the need for adapting the control software also results from changes in other disciplines such as mechanical or electrical/electronics. The various drawbacks on software maintainability, that are provoked through clone-andown, call for a shift to modular development. As a first step to realize this migration, software projects need to be analyzed in terms of variability. Secondly, visualization patterns reflecting variability are needed to present the analysis results to domain experts. However, choosing an appropriate visualization is challenging as different domain experts pursue different aims, which should be supported by the visualization and might even require different levels of detail. In this paper, visualization patterns for three different use scenarios are proposed and evaluated using an apprentice group and industrial expert feedback.

关键词： software Visualization Data visualization Automation IEC Standards Production systems

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

引用

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Establishing software product lines from existing products based on feature model recovery and merging 7

Establishing software product lines from existing products b...

引用

7th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2018

作者： Vranić, Valentino Granec, Michal Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovicova 2 Bratislava84216 Slovakia

ISBN: (纸本)9788670314733

This paper introduces an approach to establishing software product lines from a set of existing products based on a reversed Lee Kang design feature based approach to deriving program code from features, Van den Broek's feature model merging combined with partial feature models, and aspect-oriented refactoring strategies cataloged by Monteiro and Fernandes for implementing the features as modules that can be plugged in or out as needed. The approach interlinks the feature model merging cases with the actual implementation of features and speciffic aspect-oriented refactoring strategies used for this. The approach was successfully applied to JForum, establishing a product line from three existing products prepared out of the original, publicly available JForum code. © 2018 by the paper's authors.

关键词： Merging

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