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arXiv 2023年

作者： Conde, Pedro Barros, Tiago Lopes, Rui L. Premebida, Cristiano Nunes, Urbano J. The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Critical Software South Africa

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, named M-ATTA and V-ATTA) based on test time augmentation, to improve the uncertainty calibration of deep models for image classification. By leveraging an adaptive weighting system, M/V-ATTA improves uncertainty calibration without affecting the model’s accuracy. The performance of these techniques is evaluated by considering diverse metrics related to uncertainty calibration, demonstrating their robustness. Empirical results, obtained on CIFAR-10, CIFAR-100, Aerial Image Dataset, as well as in two different scenarios under distribution-shift, indicate that the proposed methods outperform several state-of-the-art post-hoc calibration techniques. Furthermore, the methods proposed also show improvements in terms of predictive entropy on out-of-distribution samples. Code for M/V-ATTA available at: https://***/pedrormconde/MV-ATTA Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Design, Modeling and Simulation of Cybercriminal Personality-Based Cyberattack Campaigns

Design, Modeling and Simulation of Cybercriminal Personality...

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Simulation Winter Conference

作者： Jeongkeun Shin Geoffrey B. Dobson L. Richard Carley Kathleen M. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Cybersecurity challenges are inherently complex, characterized by both advanced technical elements and complex aspects of human cognition. Although extensive research has explored how victims' human factors affect their susceptibility to cyberattacks, the influence of cybercriminals' personalities on the pattern of cyberattack campaigns and the resulting damage to organizations has not received equivalent attention. To bridge this research gap through computer simulation, we introduce a cyberattack campaign designer that enables modelers to construct cyberattack campaigns. This tool allows modelers to define how the process or pattern of the cyberattack campaign can change based on the cybercriminal's personality and simulates how these personality differences influence the magnitude of cyberattack damage on the target organization. In this paper, through computer simulation, we demonstrate how cautious and reckless personalities result in variations in the cyberattack pattern and, consequently, affect the magnitude of cyberattack damage, despite identical cyberattack objectives and techniques used at each step.

关键词： Bridges Computational modeling computer simulation Organizations Human factors Cognition computer crime

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STEP UP: Systematically Motivating the Children with Low Psychological Maturity Level and Disabled Children using Gamification and Human computer Interaction 7

STEP UP: Systematically Motivating the Children with Low Psy...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Dharmarathne, R.S.C.K. Medagedara, K.A. Madhubashinee, N.B.W.N. Maitipe, P.T. Sriyaratna, Disni Abeywardena, Kavinga Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665421683

Children are the future of this world. Therefore, teaching them to have a better future is very important. Also, as the adults we have to motivate them to overcome the obstacles and challenges they face throughout their lifetime. When considering about the children, there are various types of children in our society. As examples there are children with special needs and there are children who are mentally and physically stable. Children with special needs require special attention than the other children. These kinds of children with special needs have various types of development disabilities. They are children with low psychological maturity level, autism, down syndrome, genetic disorders etc. We have proposed a system to motivate these children who are with low psychological maturity level named as 'STEP-UP'. This system is a combination of four individual modules that have the common goal to motivate these kinds of children which were implemented using gamification, Image processing, machine learning and Human computer Interaction. One individual module is focused on disabled children, and it will motivate those children using gamification. Another two modules are focusing on the children who are with low psychological maturity level. And that will motivate those kinds of children using gamification and HCI based technologies like Virtual Reality. The other module is a protocol to secure the data sent between the system and the database. The common goal of this overall STEP-UP system is to motivate the children with low psychological maturity level and disabled children. © 2022 IEEE.

关键词： Machine learning

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Ontological Model for Cohesive Smart Health Services Management

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computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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Deep Learning-Based Operators for Evolutionary Algorithms

arXiv

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arXiv 2024年

作者： Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of deep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERT mutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual's fitness. We show the efficacy of both operators through experimentation. © 2024, CC BY.

关键词： Genetic programming

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White Box Testing Module for Evaluation of Transformers-Based DNNs

White Box Testing Module for Evaluation of Transformers-Base...

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Innovations in Intelligent systems and Applications Conference

作者： Gokhan Cetiner Cem Baglum Ugur Yayan Ahmet Yazici Computer Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye Software Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Artificial intelligence is actively becoming widespread in every field. The complex structure of deep neural network models, especially those used in real-world applications, may contain faults that lead to unexpected model behavior, even well-trained AI models. To detect these types of faults and model behaviors, it is necessary to perform a white box test of the model's reliability and adequacy by mutating its layers, activation functions, and similar model structures and parameters. White box testing is a method that tests the logical flow of software to detect possible faults and deficiencies in the code. The proposed module performs model-based testing using this testing technique. The module was developed as an extension of the traditional testing tool IM-FIT. It was inspired by IM-FIT's advanced fault detection capacity in traditional software testing, autonomous or customized testing options, and detailed report generation capabilities. The module carries out an advanced scanning and mutation process using its own unique fault library for deep neural networks. The module successfully performed the test by autonomously generating mutants and classifying these mutants. Tests were performed on a Transformer model using the TensorFlow library during the testing process. As a result of the tests performed with 5, 10, and 20 epoch values, a total of 606 mutants were produced. While 414 of these mutants were classified as killed, 192 were classified as survived. This study presents it to the literature as an effective mutation testing tool focusing on deep neural networks.

关键词： software testing Technological innovation Fault detection Focusing Artificial neural networks Transformers Libraries software reliability Intelligent systems Glass box

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LED there be DoS: Exploiting variable bitrate IP cameras for network DoS

arXiv

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arXiv 2025年

作者： Goldberg, Emmanuel Brodt, Oleg Elyashar, Aviad Puzis, Rami Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Israel

Variable-bitrate video streaming is ubiquitous in video surveillance and CCTV, enabling high-quality video streaming while conserving network bandwidth. However, as the name suggests, variable-bitrate IP cameras can generate sharp traffic spikes depending on the dynamics of the visual input. In this paper, we show that the effectiveness of video compression can be reduced by up to 6× using a simple laser LED pointing at a variable-bitrate IP camera, forcing the camera to generate excessive network traffic. Experiments with IP cameras connected to wired and wireless networks indicate that a laser attack on a single camera can cause significant packet loss in systems sharing the network with the camera and reduce the available bandwidth of a shared network link by 90%. This attack represents a new class of cyber-physical attacks that manipulate variable bitrate devices through changes in the physical environment without a digital presence on the device or on the network. We also analyze the broader view of multidimensional cyberattacks that involve both the physical and digital realms and present a taxonomy that categorizes attacks based on their direction of influence (physical-to-digital or digital-to-physical) and their method of operation (environment-driven or device-driven), highlighting multiple areas for future research. © 2025, CC BY-NC-ND.

关键词： Video streaming

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Alzheimer's Disease Classification Approach Using MRI Imaging

Alzheimer's Disease Classification Approach Using MRI Imagin...

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

作者： Eman Khaled Farah Amr Farah Essam Maryam Nouh Youssef Sherif Zyad Yakan Ziad El Gayar Ashraf AbdelRaouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Scinece Misr International University Cairo Egypt

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped Dataset with features extracted from the CNN model we provided an

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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