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A Bayesian Network Approach to Measure the Impact of Factors Characterizing the State of Educational Institutions on the Gross Domestic Product 13

A Bayesian Network Approach to Measure the Impact of Factors...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Voronenko, Mariia Mikhailova, Elizaveta Savina, Nataliia Lurie, Iryna Lytvynenko, Volodymyr Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine National University of Water and Environmental Engineering Department of Economic Theories Rivne Ukraine

The crisis in the field of higher education occurs at all educational levels and is manifested in insufficient material and technical equipment, in the form of unequal access to high-quality learning programs, which in turn leads to an imbalance between the acquired skills and the existing demand on the labor market. Therefore, the need to develop a technology that, taking into account certain factors, will reduce the impact of the economic crisis on the state of the education sector, is relevant. The paper proposes a guidance for developing a Bayesian network to measure the impact of factors characterizing the state of tuitional foundations on the country's gross domestic product (GDP). The developed model makes it possible to identify combinations of the most significant aspects and develop strategies to reduce the economic crisis's consequences in the education field. The results of the modelling reaffirmed that to reduce the consequences of the economic crisis in the field of tuition, it's desirable to increase investment in research and development, which will ultimately lead to an increase in the degree of GDP. © 2023 IEEE.

关键词： Bayesian networks

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Reference-free Axial Super-resolution of 3D Microscopy Images using Implicit Neural Representation with a 2D Diffusion Prior

arXiv

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arXiv 2024年

作者： Lee, Kyungryun Jeong, Won-Ki Korea University College of Informatics Department of Computer Science and Engineering Korea Republic of

Analysis and visualization of 3D microscopy images pose challenges due to anisotropic axial resolution, demanding volumetric super-resolution along the axial direction. While training a learning-based 3D super-resolution model seems to be a straightforward solution, it requires ground truth isotropic volumes and suffers from the curse of dimensionality. Therefore, existing methods utilize 2D neural networks to reconstruct each axial slice, eventually piecing together the entire volume. However, reconstructing each slice in the pixel domain fails to give consistent reconstruction in all directions leading to misalignment artifacts. In this work, we present a reconstruction framework based on implicit neural representation (INR), which allows 3D coherency even when optimized by independent axial slices in a batch-wise manner. Our method optimizes a continuous volumetric representation from low-resolution axial slices, using a 2D diffusion prior trained on high-resolution lateral slices without requiring isotropic volumes. Through experiments on real and synthetic anisotropic microscopy images, we demonstrate that our method surpasses other state-of-the-art reconstruction methods. The source code is available on GitHub: https://***/hvcl/INR-diffusion. © 2024, CC BY-NC-ND.

关键词： Anisotropy

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Explainable Artificial Intelligence to Enhance Data Trustworthiness in Crowd-Sensing Systems 19

Explainable Artificial Intelligence to Enhance Data Trustwor...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Afzal-Houshmand, Sam Papamartzivanos, Dimitrios Homayoun, Sajad Veliou, Entso Jensen, Christian D. Voulodimos, Athanasios Giannetsos, Thanassis Cyber Security Section Denmark Ubitech Ltd. Digital Security and Trusted Computing Group Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Electrical and Computer Engineering National Technical Univ. of Athens Greece

ISBN: (纸本)9798350346497

Around the world there has been an advancement of IoT edge devices, that in turn have enabled the collection of rich datasets as part of the Mobile Crowd Sensing (MCS) paradigm, which in practice is implemented in a variety of safety critical applications. In spite of the advantages of such datasets, there exists an inherent data trustworthiness challenge due to the interference of malevolent actors. In this context, there has been a great body of proposed solutions which capitalize on conventional machine algorithms for sifting through faulty data without any assumptions on the trustworthiness of the source. However, there is still a number of open issues, such as how to cope with strong colluding adversaries, while in parallel managing efficiently the sizable influx of user data. In this work we suggest that the usage of explainable artificial intelligence (XAI) can lead to even more efficient performance as we tackle the limitation of conventional black box models, by enabling the understanding and interpretation of a model's operation. Our approach enables the reasoning of the model's accuracy in the presence of adversaries and has the ability to shun out faulty or malicious data, thus, enhancing the model's adaptation process. To this end, we provide a prototype implementation coupled with a detailed performance evaluation under different scenarios of attacks, employing both real and synthetic datasets. Our results suggest that the use of XAI leads to improved performance compared to other existing schemes. © 2023 IEEE.

关键词： Time series analysis

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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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Proceedings of the International Convention MIPRO

作者： Saša Brdnik Boštjan Šumak Institute of Informatics Faculty of Electrical Engineering and Computer Science University of Maribor (Slovenia)

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and Science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field.

关键词： Surveys Ethics Systematics Explainable AI Reviews Bibliographies Market research

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Evaluation of a Serious Game for Young Software engineering Graduates: Interviewing Skills and Soft Skills

Evaluation of a Serious Game for Young Software Engineering ...

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IEEE Education engineering (EDUCON)

作者： Semira Maria Evangelou Vangelis Kapsoulakis Michalis Xenos Computer engineering and Informatics University of Patras Patras Greece

Serious Games have proved to have instructional potential due to their ability to present realistic simulations of real-life situations. In this study, a serious game called “Galaxy Solutions” was developed. This game simulates all the stages of the recruitment process. It starts when the player is searching for vacancies and submitting applications, continues to the interview process, and concludes at the last stage, in which a company named “Jupiter Solutions” hires the player. Consequently, new challenges unfold in the working environment. The main objective of this game is to help graduates acquire basic interview skills and be aware of the whole process by making the learning experience more enjoyable and engaging. Throughout the game, the player must make decisions through multiple-choice actions and dialogue, and all these decisions affect the character's events in the future. The game was evaluated by 105 students, and it was found usable by all the students, regardless of their success level in the game, and had a statistically significant learning effect, as the pre-posttest analysis revealed.

关键词： Jupiter Companies Software Serious games Interviews engineering education Software engineering

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A Compact Pipelined Architecture of Chaotic Logistic Map for e-Health Applications

A Compact Pipelined Architecture of Chaotic Logistic Map for...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Kyriaki Tsantikidou Nicolas Sklavos Computer Engineering and Informatics Department (CEID) SCYTALE Group University of Patras Hellas

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

A secure architecture of e-Health applications must be carefully approached to ensure confidentiality, integrity, and authenticity. A malicious attack or privacy breach can have serious consequences, according to the General Data Protection Regulation (GDPR). The security protocols and their cryptographic mechanisms that are commonly employed are often high resource and high execution demanding. Specifically, for the proper implementation of the key generation and the handshake protocol, various random values must be produced. This is ensured by Pseudo Random Number Generators (PRNGs) that must be compact with efficient security and verified randomness. In this paper, a compact pipelined architecture of the chaotic logistic map for e-Health applications is proposed. The proposed architecture presents a 10.82% area reduction and a 14% area delay reduction compared to other published related works. These results are achieved without the utilization of DSPs, an approach that is not followed by the other published works, while being verified by NIST tests.

关键词： Protocols Circuits and systems NIST Privacy breach Generators Delays Resource management

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Distributed Aerial 3D Object Mapping Reconstruction Using Message Passing Interface

Distributed Aerial 3D Object Mapping Reconstruction Using Me...

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International Electronics Symposium (IES)

作者： Cahyo Arissabarno Idris Winarno Sritrusta Sukaridhoto Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Aerial reconstruction of 3D object mapping involves utilizing drones or Unmanned Aerial Vehicles (UAVs) equipped with cameras to capture images for high-resolution 3D model reconstruction of various objects and terrains. This technique is applicable across various fields, such as Urban Planning, Vehicle Navigation, Environmental Studies, Civil engineering, and Cultural Heritage Conservation. Drones capture hundreds of overlapping aerial photos, which are then processed to generate photorealistic 3D representations of topographic surfaces using photogrammetry software. However, the reconstruction process is time-consuming and computationally intensive, especially when processing large datasets. This research addresses these challenges by employing parallel computing using the Message Passing Interface (MPI) to distribute the computational workload across multiple nodes. By utilizing MPI, the computational tasks are efficiently divided among four nodes, significantly reducing processing time and alleviating the burden on individual CPUs. The study demonstrates that parallel computing with MPI reduces the reconstruction time from 74 minutes to 27 minutes, achieving a speedup of 2.74 times. This research demonstrates a novel application of MPI to the domain of aerial 3D reconstruction, achieving a specific and substantial reduction in processing time. Additionally, it provides insights into future enhancements, such as parallel texture reconstruction and automation of translation processes.

关键词： Surface reconstruction Three-dimensional displays Message passing Urban planning Parallel processing Software Surface topography

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Confronting and intrusion detection techniques of cyber-attacks in wired and wireless communication networks 22

Confronting and intrusion detection techniques of cyber-atta...

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Proceedings of the 26th Pan-Hellenic Conference on informatics

作者： Konstantinos Mavrommatis Informatics & Computer Engineering University of West Attica Greece

ISBN: (纸本)9781450398541

This publication aims to study the attacks that telecommunications networks face every day. Thus, the tools that have been developed through new technologies are presented in detail in order to successfully deal with these attacks. Some of these tools are more effective and others less effective depending on the form of attack. As a conclusion is that very significant steps have been taken in confronting with attacks by intruders by introducing a new and wide field of study in the networks.

关键词： War Driving MAC Spoofing TCP Syn Flood Rogue AP Evil Twin Attack Cash Poisoning Sniffing IP Spoofing DDoS

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Satu Sehat Interoperability Agent

Satu Sehat Interoperability Agent

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Hero Yudo Martono Andhik Ampuh Yunanto Dian Septiani Santoso Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

Data integration from all health care facilities in Indonesia to the Satu Sehat platform is an obligation that must be carried out, but it turns out that until early October 2024, around 57% of health care facilities have not succeeded in carrying out this integration. With this condition, it can be concluded that there are difficulties for health care facilities to carry out integration and this is estimated to be due to differences in data formats between health care facilities and Satu Sehat. For this reason, an interoperability agent is needed to bridge health care facilities with Satu Sehat. We built the first version of the interoperability agent that bridges this integration. This agent works by entering the client database, searching for the appropriate data in each health care facility, creating a data format that matches the Satu Sehat format, and then sending the format to Satu Sehat. This agent application will evolve over time. For the first version, this interoperability agent has been able to produce Satu Sehat data formats for eight types of resource types including encounter, procedure, condition, composition, medication, medication_request and medication_dispense. Trials have been carried out on each resource type and have run smoothly. In the future, this interoperability agent will be developed so that the number of resource types is in accordance with that determined by Satu Sehat.

关键词： Bridges Technological innovation Translation Databases Medical services Information technology Interoperability Standards Monitoring Payloads

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Pulmonary Diseases Decision Support System Using Deep Learning Approach

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computers, Materials & Continua 2022年第10期73卷 311-326页

作者： Yazan Al-Issa Ali Mohammad Alqudah Hiam Alquran Ahmed Al Issa Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid 22110Jordan Pediatrician Mediclinic HospitalAl Ain14444UAE

Pulmonary diseases are common throughout the world,especially in developing *** diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently *** general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert *** recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung *** this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was *** the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)*** models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross *** individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.

关键词： Pulmonary diseases deep learning lung opacity classification majority voting ensemble features

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