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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Yavuz, Sinan Naroska, Edwin Daniel, Kai University of Siegen Department of Electrical Engineering and Computer Science Siegen Germany Niederrhein University of Applied Sciences Department of Electrical Engineering and Computer Science Krefeld Germany Institute of Measurement and Sensor Technology University of Applied Sciences Ruhr West Mülheim Germany

ISBN: (纸本)9798331540289

The security of IoT (Internet of Things) devices and the protection of sensitive information processed by these devices such as personal data, sensor values, process-related information is an important and difficult challenge. A major task in IoT communication is secure identification of devices. Unfortunately, traditional cryptographic methods are often not suitable for IoT devices due to their limited hardware resources. However, typical methods are computationally intensive, require a large amount of memory, and have a high-power consumption. On the other hand, Physical Unclonable Functions (PUFs) are low-cost and lightweight hardware-based primitives that can be used as a security component to protect data against third parties and thus increase security of a device. In this paper, the challenges and vulnerabilities in the development of PUF-based authentication protocols are presented. To this end, a security analysis of different approaches known from literature are discussed. Furthermore, possible attack vectors and prevention techniques are also considered. © 2024 IEEE.

关键词： Authentication Protocol

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On Criteria and Tooling for Cryptographic Inventories

On Criteria and Tooling for Cryptographic Inventories

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Die Fachtagung Sicherheit, Schutz und Zuverlassigkeit, SICHERHEIT 2024 - Security, Protection and Reliability Conference, SICHERHEIT 2024

作者： Schmitt, Nicolai Henrich, Johanna Heinz, Dominik Alnahawi, Nouri Wiesmaier, Alexander Darmstadt University of Applied Sciences Department of Computer Science and Information Technology Schöfferstraße 3 Darmstadt64295 Germany

ISBN: (纸本)9783885797395

When cryptography becomes insecure, a migration to new schemes is required. Often the migration process is very complicated, but the time available is very limited. Only if the used cryptographic algorithms, protocols and configurations are known can a system be efficiently and fully adapted to changed security situations. This creates the need for a crypto-inventory that gathers this knowledge. Consequently, the question arises what criteria a crypto-inventory must fulfill to support this adaptation. It also highlights the need for tools to assist compilation. We therefore conducted a literature survey and extracted key requirements. Missing content was supplemented by expanding existing requirements or adding new ones. Furthermore, appropriate metrics were assigned to assess the fulfillment of the requirements for a certain crypto-inventory implementation. Regarding the tooling, we identified five major areas of interest - installed software, connected hardware, communication, stored data and source code scanning - and provide prototypes for semi-automatic creation of crypto-inventories for three of them. This provides organizations with a starting point to understand their cryptographic landscape as a prerequisite for crypto-agility and crypto-migration. However, theoretical design and prototypes have not yet been evaluated. This will be done as a follow-up to this work. All types of organizations are invited to participate. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Cryptography

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Dingzhou Huang, Guoheng Yuan, Xiaochen Chen, Xuhang Zhong, Guo Pun, Chi-Man Deng, Jie Guangdong University of Technology School of Computer Science and Technology China Macao Polytechnic University Faculty of Applied Sciences China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Guangdong University of Foreign Studies School of Information Science and Technology China First Affiliated Hospital of Sun Yat-Sen University China

ISBN: (纸本)9798350386226

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual Information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model's performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies. © 2024 IEEE.

关键词： Endoscopy

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Genetic Algorithm Based Adaptive FSO Communication Link 2

Genetic Algorithm Based Adaptive FSO Communication Link

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Abualkishik, Abedallah Saleem, Muhammad Farooq, Umer Asif, Muhammad Hassan, Muhammad Malik, Javaid Ahmad American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan LGU Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

Free Space Optical (FSO) communication systems are used to transmit high data rates over short distances through the atmosphere. However, the performance of FSO communication links can be severely impacted by atmospheric conditions, such as fog, rain, and atmospheric turbulence. These conditions cause temporary and spatial changes in light intensity, which can lead to signal degradation and disruption. To ensure reliable FSO communication, it is important to take into account the meteorological conditions of the location where the FSO communication link will be deployed. This will help in determining the expected level of link availability and identifying potential problems that could arise due to atmospheric conditions. For example, if a location is prone to fog or heavy rain, it may not be suitable for FSO communication link. One key factor in the design and operation of FSO communication links is the Geometrical Losses (GL), which refer to the loss of power due to the divergence of the laser beam as it propagates through the atmosphere. This can be minimized by increasing the diameter of the receiver aperture, which allows more of the laser beam to be collected and directed towards the *** this paper, the researchers used a Genetic Algorithm (GA) to minimize GLs in the FSO communication link. The GA is an optimization technique that mimics the process of natural selection to find the best solution to a problem. In this case, the GA was used to find the optimal diameter of the receiver aperture that would minimize GLs. The researchers used the optimum tool in MATLAB 2017 to implement the GA and found that after a certain number of iterations, the GLs remained constant. This was considered to be the optimal diameter of the receiver aperture. © 2023 IEEE.

关键词： Genetic algorithms

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From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization 30

From News to Summaries: Building a Hungarian Corpus for Extr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Barta, Botond Lakatos, Dorina Nagy, Attila Nyist, Milán Konor Ács, Judit HUN-REN Institute for Computer Science and Control Hungary Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary

ISBN: (纸本)9782493814104

Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning, preprocessing and deduplication. In addition to abstractive summarization we generate sentence-level labels for extractive summarization using sentence similarity. We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation. Our dataset, models and code are publicly available, encouraging replication, further research, and real-world applications across various domains. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： abstractive summarization extractive summarization Hungarian

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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Case Study Based Investigation on Self-Healing Cloud Deployments for Edge-Based Software Development

Case Study Based Investigation on Self-Healing Cloud Deploym...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Shabariram, C.P. Vrinda, S. Srivatsan, S. Manimeghalai, R. PSG Institute of Technology and Applied Research Department of Computer Science and Engg Coimbatore India Sap Labs Karnataka Bengaluru India

ISBN: (纸本)9798350378177

Cloud Computing has enabled many application providers to deploy web applications in cloud data centers with the advent of on-demand resource allocation and self-healing techniques that provide high availability and flexibility. However, web applications frequently have dynamic workloads that are difficult to forecast. Researchers and Cloud service providers are working hard to find ways to cut costs without sacrificing Quality of Service (QoS). Self-healing is one of the key issues in Cloud computing for application deployment, which is still in its early phases and necessitates extensive research on taxonomy, methodologies, and resource requirements. This article provides a complete overview of self-healing approaches for cloud application deployment. This work aims to assist the scientific community in identifying the elements required for efficient self-healing strategies. An overview of evaluated studies is presented, including various self-healing strategies, resources, monitoring tools, experiments, workloads, metrics, and other pertinent factors. This work provides useful insights and lays the groundwork for future research in this area. In this work, an existing platform is deployed using several self-healing deployment methodologies and responses from various stakeholders are collected and analyzed. The analysis of stakeholders response indicates that self-healing strategies provide better acceptance and deployment with increased customer delight. After self-healing cloud deployment. The survey result indicates self healing improves issue detection, down time and overall system reliability by 90%, 85% and 70%. The operational overhead was reduced by 70%. © 2024 IEEE.

关键词： Cloud platforms

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Generative AI and Gamification for Personalized Learning: Literature Review and Future Challenges

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SN computer science 2024年第8期5卷 1-12页

作者： Abbes, Feriel Bennani, Souha Maalel, Ahmed RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia University of Sousse Higher Institut of Applied Science and Technology Sousse Tunisia

The integration of Artificial intelligence (AI) in education (AIEd) opens the possibilities for creating new content, virtual teaching, and personalized learning experiences. This paper studies the different innovative methods used in education where we investigate the integration of Generative Artificial Intelligence (GAI) into education, and its transformative influence in producing personalized learning experiences through the use of its models like BERT, ChatGPT and GANs that have unique role in creating new content like text, image, and video, unlike discriminative algorithms that focus only on classification tasks. Also, the paper explores the power of gamification to enhance academic systems by generating new dynamic and interesting learning environments by using specific design that make learner learn and have fun which adjusts their needs, their personality, values and motivation. Even though the importance,The development of a dynamic adaption engine model for GAI and adaptive gamification is a pressing matter in this particular domain. Thus, This review aims the incorporation of generative AI into gamification to generate personalized content and improve the quality of the entire learning experience. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive gamification Adaptive learning AIED Artificial Intelligence (AI) Education Generative AI Learning Personalized learning Teaching

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Water Level Controller with Weather Forecasting Using 8051 Microcontroller 7

Water Level Controller with Weather Forecasting Using 8051 M...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Kaniskaa, M.S. Madanagopal, C. Deepa, M. Hariprasath, K. PSG Institute of Technology and Applied Research Department of Electronics and Communications India Vellore Institute of Technology and Applied Research Department of Computer Science India

ISBN: (纸本)9798350340600

Water management is a salient role of engineers due to its importance in everyday activities. The proposed work aims to eliminate over- and under-irrigation, as well as water waste, by monitoring soil moisture and enabling automatic switching of the pumping system to adjust the moisture level. Along with the soil moisture levels, the weather forecast is also considered to regulate the moisture level. The proposed design implements a water level controller by tracking the soil moisture levels using an 8051 microcontroller and interfacing it with sensors. A threshold value is set, for both lower and higher values of soil moisture and the live value is constantly compared. When the value is below or above the respective lower and higher threshold values, the motor pumping system is automatically turned on or off respectively. The output values are observed using a web application via an Arduino board. The design also uses Node MCU which is integrated with ThingSpeak to aggregate and store data. The web application has an additional feature of weather forecasts, which is live data obtained using an Application Programming Interface (API) key with Weatherstack API. This weather forecast is used to control the motor and automatically change its state based on the soil's present state of moisture level and the weather that day. This feature makes the design more reliable and practical for integrating it with mainstream agriculture and gardening activities. © 2023 IEEE.

关键词： Soil moisture

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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