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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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BIM-based Digital Transformation in Underground Construction: Two Application-based Research Projects

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BHM Berg- und Huttenmannische Monatshefte 2020年第12期165卷 658-665页

作者： Mazak-Huemer, Alexandra Galler, Robert Wenighofer, Robert Vierhauser, Michael Huemer, Christian Lehrstuhl für Subsurface Engineering – Digital Transformation in Tunnelling Montanuniversität Leoben Erzherzog-Johann-Strasse 3 Leoben8700 Austria Institut für Wirtschaftsingenieurwesen – Software Engineering Johannes Kepler Universität Linz Linz Austria Institute of Information Systems Engineering Business Informatics Group Technische Universität Wien Wien Austria

There are numerous publications propagating the digital transformation in the field of civil engineering in general and tunnelling in specific. Building Information Modeling (BIM) is frequently mentioned as a promising approach. Thus, BIM is the method of choice for realizing the digital transformation. However, the prototypical character of a tunnel project leads to difficulties in implementing a standardized approach. Additionally, the various disciplines involved employ individual software tools with proprietary (non-standardized) data formats for different tasks within an underground construction project. Such self-contained data models hamper the implementation of Open BIM. In order to tackle these challenges, the Chair of Subsurface engineering at the University of Leoben has launched two application-oriented research projects for digital transformation in civil engineering and tunnelling. These two projects represent the core of this article. The "Zentrum am Berg (ZaB)" serves for both projects as central infrastructure for evaluation purposes. © 2020, The Author(s).

关键词： Standardization

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Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation

arXiv

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arXiv 2024年

作者： Feng, Yifan Huang, Jiangang Du, Shaoyi Ying, Shihui Yong, Jun-Hai Li, Yipeng Ding, Guiguang Ji, Rongrong Gao, Yue School of Software BNRist THUIBCS BLBCI Tsinghua University Beijing100084 China Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi’an Jiaotong University Xi’an710049 China The Department of Mathematics School of Science Shanghai University Shanghai200444 China The Department of Automation Tsinghua University Beijing100084 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Institute of Artificial Intelligence Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Xiamen University 361005 China

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOLO models, while powerful, have limitations in their neck designs that restrict the integration of cross-level features and the exploitation of high-order feature interrelationships. To address these challenges, we propose the Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation. This enables the model to acquire both semantic and structural information, advancing beyond conventional feature-focused learning. Hyper-YOLO incorporates the proposed Mixed Aggregation Network (MANet) in its backbone for enhanced feature extraction and introduces the Hypergraph-Based Cross-Level and Cross-Position Representation Network (HyperC2Net) in its neck. HyperC2Net operates across five scales and breaks free from traditional grid structures, allowing for sophisticated high-order interactions across levels and positions. This synergy of components positions Hyper-YOLO as a state-of-the-art architecture in various scale models, as evidenced by its superior performance on the COCO dataset. Specifically, HyperYOLO-N significantly outperforms the advanced YOLOv8-N and YOLOv9-T with 12% APval and 9% APval improvements. The source codes are at https://***/iMoonLab/Hyper-YOLO. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Mindset-Shaping Solutions with Innovative IT and PM Technologies in Higher Education

Mindset-Shaping Solutions with Innovative IT and PM Technolo...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Mariann Vàraljai Enikő Nagy Bàlint Nagy Zoltàn Balogh György Molnàr Department of Software Development and Application University of Dunaújváros Dunaújváros Hungary John von Neumann Faculty Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest Hungary Department of Mathematics Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary Department of Natural Sciences Faculty of Electrical Engineering Institute of Electrophysics Kandó Kálmán Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary Apáczai Csere János Faculty of Humanities Education and Social Sciences Széchenyi University Győr Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Every society has its own characteristics, shaped from time to time by the various innovative, disruptive, but in any case, dynamically changing mechanisms that affect it most. In today's society, technological innovation plays a major role in the development of individuals and their abilities. Education is the most typical and obvious arena for shaping, developing, and preparing for the challenges of the future. As in the leading sectors, in the world of education, rapid responses and immediate reactions to change significantly increase the ability to achieve and maintain success and thus marketability. As a sector responsible for developing the competences of individuals, building knowledge and training professionals, education should seek to keep pace with trends and break with traditional approaches and methods, so that, in addition to incorporating technological developments, a renewed approach to organizational development and methodology is an important means of achieving the desired success and effectiveness. In the course of our work, we provide a summary insight into the project work of student groups who require up-to-date IT skills, use advanced IT tools, plan, and implement their web development workflows supported by database management, front-end planning and design, and back-end programming with a project management approach according to the latest trends, interpreting the advantages and disadvantages of working methods for success.

关键词： Training Knowledge engineering Technological innovation Shape Project management Market research Planning

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Supervised classification using graph-based space partitioning for multiclass problems 25

Supervised classification using graph-based space partitioni...

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25th International Conference on Pattern Recognition, ICPR 2020

作者： Yanev, Nicola Valev, Ventzeslav Suliman, Karima Ben Krzyzak, Adam Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9781728188089

We introduce and investigate in multiclass setting an efficient classifier which partitions the training data by means of multidimensional parallelepipeds called boxes. We show that multiclass classification problem at hand can be solved by combining the heuristic minimum clique cover approach and the k-nearest neighbor rule. Our algorithm is motivated by an algorithm for partitioning a graph into a minimal number of cliques. The main advantage of a new classifier called Box classifier is that it optimally utilizes the geometrical structure of the training set by reducing the l-class classification problem to a single nearest neighbor problem. We discuss computational complexity of the proposed Box classifier. The extensive experiments performed on the simulated and real data from UCI Machine Learning Repository for binary and multiclass problems show that in almost all cases the Box classifier performs significantly better than k-NN, SVM and decision trees. © 2020 IEEE

关键词： Decision trees

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Managing Inter-Model Inconsistencies in Model-based Systems engineering: Application in Automated Production Systems engineering

Managing Inter-Model Inconsistencies in Model-based Systems ...

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Fachtagung des GI-Fachbereichs softwaretechnik, software engineering 2020 - Conference of the GI Special Interest Group on software engineering, software engineering 2020

作者： Feldmann, Stefan Kernschmidt, Konstantin Wimmer, Manuel Vogel-Heuser, Birgit Automation and Information Systems Technical University of Munich Boltzmannstr. 15 Garching near Munich85748 Germany Institute of Business Informatics - Software Engineering JKU Linz Altenbergerstr. 69 Linz4040 Austria

This work summarizes our paper [Fe19] originally published in the Journal of Systems and software in 2019 about a model-based inconsistency management approach. © 2020 Gesellschaft fur Informatik (GI). All rights... 详细信息

ISBN: (纸本)9783885796947

关键词： Systems engineering

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information Sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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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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When pedestrian detection meets nighttime surveillance: A new benchmark 29

When pedestrian detection meets nighttime surveillance: A ne...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Xiao Chen, Jun Wang, Zheng Liu, Wu Satoh, Shin'ichi Liang, Chao Lin, Chia-Wen Wuhan University China National Institute of Informatics AI Research of *** National Tsing Hua University Taiwan National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Collaborative Innovation Center of Geospatial Technology China

ISBN: (纸本)9780999241165

Pedestrian detection at nighttime is a crucial and frontier problem in surveillance, but has not been well explored by the computer vision and artificial intelligence communities. Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances. In contrast, they often fail under unstable lighting conditions (e.g. nighttime). Night is a critical time for criminal suspects to act in the field of security. The existing nighttime pedestrian detection dataset is captured by a car camera, specially designed for autonomous driving scenarios. The dataset for nighttime surveillance scenario is still vacant. There are vast differences between autonomous driving and surveillance, including viewpoint and illumination. In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of NightSurveillance. We believe that NightSurveillance can further advance the research of pedestrian detection, especially in the field of surveillance security at nighttime. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Lighting

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Test Data Generation for MC/DC Criterion using Reinforcement Learning

Test Data Generation for MC/DC Criterion using Reinforcement...

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IEEE International Conference on software Testing Verification and Validation Workshop, ICSTW

作者： Ján Čegiň Karol Rástočný Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9781728110752

ISBN: (纸本)9781728110769

Unit testing focused on MC/DC criterion is essential in development of safety-critical systems. However design of test data that meet the MC/DC criterion needs detailed manual analysis of branching in units under test by test engineers. To deal with this problem we propose a new test data generation approach based on reinforcement learning, which utilize analogy with a game, in which a gamer, the test engineer, plays in an environment, a unit under test, and tries to achieve the highest possible reward, MC/DC coverage. We evaluated our approach for two different granularity levels, test suite and test case, and for two different action types allowed to the gamer, discrete and continuous action spaces. Preliminary results shows that the proposed approach could solve path explosion problem of symbolic approaches and that the proposed approach achieves at least comparable results to the current state-of-the-art search-based test data generation approaches.

关键词： Testing Neural networks Tools Learning (artificial intelligence) Explosions Prototypes Complexity theory

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