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arXiv 2024年

作者： Yatkın, Simge Ovatman, Tolga Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey MGEO Test and Verification Directorate ASELSAN Inc. Ankara Turkey

DO-178C stands out as a guiding standard for aviation system development processes. This standard not only mandates ensuring the consistency of requirements in the software verification process but also recognizes it as a mandatory element. The main objective of this study is to introduce a method for analyzing and identifying inconsistencies between high-level requirements using information obtained from a data dictionary. This method aims to transform high-level requirements into logical expressions and then thoroughly examine them using a SAT Solver to detect inconsistencies. While methods focused on identifying inconsistencies among requirements often appear in the literature, this study presents a novel approach to detect contradictions between non-natural language, systematically structured, and language-independent requirements. The goal of this approach is to significantly reduce the review time of high-level requirements in the software verification process. Evaluations indicate that the use of this method results in substantial time savings in the inconsistency detection process. © 2024, CC BY-SA.

关键词： Model checking

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IoT Educational Management and Consultancy System for Egyptian Public Schools

IoT Educational Management and Consultancy System for Egypti...

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AFRICON Conference

作者： Omar Ashraf Saleh Salma Elsherif Dina Mohamed Sherif Nada Sharaf Yasmine Zaghloul Faculty of Informatics and Computer Science The German International University Cairo Egypt Faculty of Engineering The German International University Cairo Egypt Faculty of Engineering German International University Cairo Egypt

Educational systems in Egypt face a shortage in the number of teachers as there is an annual increment in the number of students who are enrolled in public schools. Therefore, voluntary teaching is proposed to assist the hierarchy of educational objectives and to compensate for this shortage. With the help of technologies such as QR code scanners, Blue-tooth, Wifi, RFID, and computer Vision, a technological system guarantees a high quality and high performance of monitoring teaching activities. Nevertheless, an implementation of a mobile application guides the volunteer, the student, and the school administrators to monitor and access the course content easily along with a rewarding system that credits the volunteers for their effort.

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A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

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computers, Materials & Continua 2021年第7期68卷 431-446页

作者： Ahmed S.Salama Mohamed Amr Mokhtar Mazhar B.Tayel Esraa Eldesouky Ahmed Ali Future University in Egypt CairoEgypt Cairo Higher Institute for Engineering Computer Science and ManagementCairoEgypt Department of Electrical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia CityEgypt Higher Future Institute for Specialized Technological Studies CairoEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Assuring medical images protection and robustness is a compulsory necessity *** this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT ***,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the ***,a PN-sequence key is formed along with a supplementary key in the third layer of the ***,the watermarked image is generated by enclosing both keys into DWT and DCT *** fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT *** order to validate the proposed technique,a standard dataset of medical images is *** results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical *** addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

关键词： Medical image processing digital image watermarking discrete wavelet transforms discrete cosine transform encryption image fusion hybrid fusion technique

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A Wideband Rotary Antenna Based on Gap Wavegudie Technology for mmWave Applications

A Wideband Rotary Antenna Based on Gap Wavegudie Technology ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ali Farahbakhsh Davood Zarifi Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran School of Electrical and Computer Engineering University of Kashan Kashan Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This study introduces the design of a gap waveguide-based rotary antenna capable of 360° mechanical beam-steering for wideband mmWave applications. This innovative design overcomes the typical limitations of electronically steerable antennas, such as narrow bandwidth, restricted steering range, gain reduction, and high cost and complexity. The designed rotary antenna achieves a gain of 13.8 dBi without any scan loss across the entire 360° steering range from 45 to 60 GHz. The proposed rotary antenna is a promising candidate for a variety of mmWave mechanical beam scanning systems.

关键词： Steerable antennas Costs Electromagnetic scattering Gap waveguide Europe Broadband antennas Impedance Millimeter wave communication Electromagnetics Wideband

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Quadratic Weighted Kappa Score Exploration in Diabetic Retinopathy Severity Classification Using EfficientNet

Quadratic Weighted Kappa Score Exploration in Diabetic Retin...

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Lincoln Chivinge Leslie Kudzai Nyandoro Kudakwashe Zvarevashe Analytics & Informatics Department Faculty of Computer Engineering Informatics and Communication University of Zimbabwe Harare Zimbabwe

ISBN: (纸本)9781665475778

Diabetic Retinopathy (DR), chronic progressive disease of the eye, may give rise to permanent sight loss. Clinicians use fundus pictures to check if DR is present and rely on physicians to diagnose the stage or severity by visual inspection of the images. In relying on a clinician’s subjective prognosis, this is deemed a procedure that takes a lot of time and susceptible to misjudgements. In discovering DR, poor Quadratic Weighted Kappa (QWK) scores have resulted from poor quality of pictures and imbalanced distribution of classes. Even though studies have shown high accuracy, sensitivity, specificity and ROC metrics, their limitation is that they do not consider the level of disparity across the classified labels. The QWK score demonstrates that even if an algorithm presents high accuracy, it is still not best fit to classify DR into its 5 classes. Many researchers have tried fine-tuning the neural network to create noise-resistant deep learning and recorded high accuracy and sensitivity but low QWK scores. The problem with the other methods is mainly pre-processing of the images and model building patterns. Most of the studied literature lacks the image augmentation step which might lead to an erroneous result. This research aims to create an algorithm from deep learning models with a data augmentation step and demonstrate how important it is for attaining better QWK scores for all stages of diabetic retinopathy. The model met study objectives and obtained an accuracy of 93% and a QWK score of 0.961. The outcomes show that the method can make accurate predictions without need for human feature extraction and that it may be used as an early DR diagnostic and staging screening tool.

关键词： Deep learning Visualization Sensitivity Retinopathy Pipelines Medical services Feature extraction

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Fake News Detection using 5L-CNN

Fake News Detection using 5L-CNN

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Demo Rangarirai Collen Leslie Kudzai Nyandoro Kudakwashe Zvarevashe Analytics & Informatics Department Faculty of Computer Engineering Informatics and Communication University of Zimbabwe Harare Zimbabwe

ISBN: (纸本)9781665475778

With the advent of websites and social media technologies, the internet has been a great method of transmitting news and information around the globe. However, due to lack of editorial scrutiny and monitoring by the media authorities, the news distribution has been seriously abused leading to the fast spreading of fake news. Fake news involves misleading broadcasting of information from sources that target to intentionally manipulate the way how people view some events or statements. This study seeks to develop a fake news detection model which will be able to analyze the title and text information attached to the news articles. From the researches done by previous scholars, it showed that the models did not perform very well because of the lack of sufficient feature extraction and fine tuning of the classification of the text. This research will therefore, articulate the gap by employing 5L-CNN deep learning model which will use inbuilt tokenizers for word embedding and will show better accuracy compared to the traditional machine learning models. In this paper, we compare machine learning algorithms (Decision trees, Random Forest, Logistic Regression & Naive Bayes) and deep learning algorithms (RNN, 5L-CNN & LSTM) to classify the authenticity of news articles. The model developed in this paper attained the best accuracy with 5L-CNN which had a result of 99.99%.

关键词： Deep learning Machine learning algorithms Social networking (online) Predictive models Streaming media Prediction algorithms Fake news

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TSAL: Two steps Adversarial learning based domain adaptation 34th

TSAL: Two steps Adversarial learning based domain adaptation

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34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021

作者： Badr, Haidi Wanas, Nayer Fayek, Magda Electronics Research Institute Informatics Dapartment Egypt Cairo university Faculty of Engineering Computer Department Egypt

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasis the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions. © 2021by the authors. All rights reserved.

关键词： Network architecture

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Performance Analysis of RouteSegmentation Algorithm in Identifying Perimeter Area Around Travel Route

Performance Analysis of RouteSegmentation Algorithm in Ident...

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International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Muhammad Avied Bachmid Muhammad Yasir Anshari Haq Muhammad Rafly Mumtaz Aryo Pinandito Media Game and Mobile Laboratory Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Faculty of Computer Science Universitas Brawijaya Malang Indonesia Media Game and Mobile Laboratory Department of Information System Faculty of Computer Science Universitas Brawijaya Malang Indonesia

ISBN: (数字)9798350375817

ISBN: (纸本)9798350375824

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency.

关键词： Meters Visualization Systematics Runtime Navigation Stability analysis Performance analysis Reliability Thermal stability Geographic information systems

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A comparative analysis of the effectiveness of feature engineering techniques on thyroid disease prediction

A comparative analysis of the effectiveness of feature engin...

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Aidah Mashonga Leslie KudzaiNyandoro Kudakwashe Zvarevashe Analytics & Informatics Department Faculty of Computer Engineering Informatics and Communication University of zimbabwe Harare Zimbabwe

ISBN: (纸本)9781665475778

The thyroid gland’s edge experiences an abnormal proliferation of thyroid tissue, which causes thyroid illness. The two primary types of thyroid disorders are hypothyroidism and hyperthyroidism which typically result when this gland releases excessive amounts of hormones. To identify and diagnose thyroid disease, this study suggests employing effective classifiers and feature selection strategies that consider accuracy and other performance evaluation measures. This study offers a thorough examination of various classifiers that includes the support vector machine, logistic regression, and extreme gradient boosting algorithms. The algorithms use three feature removal strategies that is recursive feature elimination, Pearson’s correlation and chi-squared statistics. To determine thyroid illness, thyroid data from the Kaggle datasets were used. Numerous aspects of the experiment have been evaluated and analyzed, including accuracy, precision, and the receiver operating curve’s area under the curve. The outcome showed that classifiers that use feature selection have a greater overall accuracy(Xtreme Gradient Boost 98%and support vector machine 95%) compared to without feature selection technique (support vector machine 89%). Logistics regression performed better without at 95% than 94% with feature selection.

关键词： Support vector machines Performance evaluation Correlation Receivers Feature extraction Prediction algorithms Classification algorithms

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The Impact of Interest Flooding Attack (IFA) and Collusive IFA on Named Data Networking (NDN)

The Impact of Interest Flooding Attack (IFA) and Collusive I...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Jupriyadi Eueung Mulyana Nana Rachmana Syambas School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Universitas Teknokrat Indonesia Lampung Indonesia

Named Data Networking (NDN) is a proposed future Internet architecture that has the advantage of bringing content data closer to users. NDN focuses on securing content data rather than securing transmission channels. The NDN network is also not free from threats and attacks that can disrupt network performance. One of the attacks on the NDN network is a denial of service (DoS) attack. The attacker carries out this attack by sending a large number of request packets. This attack is known as an interest flooding attack (IF A). The IF A attack itself has developed into a collusive IFA to avoid existing detection systems. In this paper, we examine the comparative impact of IF A and CIF A attacks on clients or consumers. Knowing the impact and characteristics of IF A and CIF A attacks can be used as a reference in the attack detection system. Attacks are carried out locally and remotely by looking at 3 types of attacks, namely attacks by sending requests for packets that are on the network, attacks by sending requests for containers that are not available on the net, hybrid attacks, and 1 collusive IF A attack. Based on the exploration carried out, it was found that local attacks had a very significant impact compared to remote attacks. IFA attacks that have a major impact on the network are attacks that send content that is not available on the net. The effect of this attack meant that requests sent by consumers were only served by 40% of the total bids.

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