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Knowledge-based Interactive Configuration Tool for Industrial Augmented Reality systems 5

Knowledge-based Interactive Configuration Tool for Industria...

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5th International conference on Information and computer Technologies, ICICT 2022

作者： Eckertz, Daniel Anacker, Harald Dumitrescu, Roman Systems Engineering Fraunhofer Institute for Mechatronic Systems Design Iem Paderborn Germany Product Engineering Fraunhofer Institute for Mechatronic Systems Design Iem Paderborn Germany

ISBN: (数字)9781665469609

ISBN: (纸本)9781665469609

the innovative technology Augmented Reality (AR) provides enormous potentials for companies. But due to the high technological complexity and the lack of expertise and experience in companies, the potentials are only rarely exploited. One major challenge for companies is the demand-oriented configuration of the AR system for a specific application scenario. this includes the selection of a suitable AR device and the specification of the AR application software. Relevant knowledge is only implicitly available in a variety of individual distributed sources. Companies therefore heavily rely on technology providers or experts when using AR. this work presents a novel knowledge-based approach to support companies in the individual AR system configuration. Based on a digital knowledge base an interactive configuration tool is developed. the tool enables the targeted selection of a suitable AR device and required AR software functionalities for a custom application scenario considering individual circumstantial conditions and requirements. It thereby empowers companies to configure AR systems independently and without prior expertise, facilitating the exploitation of the potentials of AR. © 2022 IEEE.

关键词： Augmented reality

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Examining the Utilization of Artificial Intelligence Tools by Students in software engineering Projects 16

Examining the Utilization of Artificial Intelligence Tools b...

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16th International conference on computer Supported Education, CSEDU 2024

作者： Dirin, Amir Laine, Teemu H. Metropolia University of Applied Sciences Helsinki Finland Department of Digital Media Ajou University Suwon Korea Republic of

ISBN: (纸本)9789897586972

With the popularity of AI-based tools, the landscape of learning and teaching software engineering has shifted to a new era, which has left both educators and students confused regarding the extent to which these tools are reliable, secure, and, most importantly, result in efficient student competence development. In this study, we explored how the use of AI tools such as ChatGPT and Github Copilot affect the performance of 36 students in nine teams in a software engineering project course. We also explore the perceptions of the students regarding the use of AI tools in software engineering. We divided the project teams into three groups based on their use of AI tools: group 1 used AI tools freely, group 2 used AI tools in a restricted manner, and group 3 did not use any AI tools. the results indicated that while all groups successfully finished their projects, AI tools were of great help in user story creation and completing a high number of features and tasks. However, groups 1 and 2 also require time to learn the AI tools and the resulting software quality was lower than that of group 3. In conclusion, AI tools like Copilot and ChatGPT can become powerful companions to software engineering students in their educational activities. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Artificial intelligence

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Top-Performing Unifying Architecture for Network Intrusion Detection in SDN Using Fully Convolutional Network 5

Top-Performing Unifying Architecture for Network Intrusion D...

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5th International conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Roy, Bishwarup Acharya, Indranil Papalkar, Darshil Joseph, Mary Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

Internet's omnipresent adoption over the course of many years leading up to the current era along with Wireless Sensor Networks' (WSN) limitations has only increased the security threats to these high-speed networks. As a result, it is critical to deploy Intrusion Detection systems (IDS) to monitor malicious activities in the network framework. software Defined Network (SDN) has served its purpose to a certain extent by enabling centralized control and management of WSN and providing ways to implement IDSs more efficiently. However, like any emerging technology, SDN also has its share of vulnerabilities as there exists a gap for contemporary architectures, which can exploit global network assets and balance network resource consumption while providing programmability in terms of security of the network. To tackle this problem, a model has been developed that uses a fitting feature selection method to cut down on the count of features extracted and minimize the data volume relayed over the routes. Conclusions drawn from the scheme's trials demonstrate the proposed IDS technology's potency and effectiveness. the openly accessible University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) was used to evaluate the intrusion detection algorithm involving the Fully Convolutional Network (FCN) architecture and its effect using a statistical technique in detecting different types of attacks. © 2023 IEEE.

关键词： Deep neural networks

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Artificial Intelligence empowered content caching for energy optimization in vehicular networks 5

Artificial Intelligence empowered content caching for energy...

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5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Gupta, Divya Bansal, Ankit Kaur, Jaspreet Wadehra, Sanchit Department of Computer Science and Engineering Chandigarh University India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798400709418

Indeed, the development of future autonomous and intelligent transportation systems depends heavily on vehicular networks. they allow vehicles to connect with one another, with roadside infrastructure, and with centralized control systems. RSUs can store regularly accessed data including map updates, traffic data, and software updates. Data caching is one of the main responsibilities of Road side Units (RSUs) in Vehicular application as sending the same content repeatedly wastes a lot of system resources. Utilizing caching resources effectively can lower the link load imposed by content transmission, thus enhancing the quality of experience (QoE) for users. However, inefficient collaboration among network routers may lead to substantial energy wastage. To address the aforementioned problem and reduce the requested content energy consumption, this paper proposes artificial intelligence empowered collaborative content caching scheme utilizing the concept of both on path caching and off path caching. Performance analyses show that the proposed technique outperforms other current caching strategies. © 2023 ACM.

关键词： Energy utilization

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Research and Application of software Development Model in computer software Development 22

Research and Application of Software Development Model in Co...

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5th International conference on Information Science and systems, ICISS 2022

作者： Han, Dan Li, Xiaojing Zhang, Jing Wang, Guangyu Deng, Hua Deng, Lin Aviation University of Air Force China

ISBN: (纸本)9781450396837

the research purpose of software development is to try to develop various software that can meet the needs of users with low cost and high reliability. At present, many new software development technologies and methods are still difficult to adapt to the main trends of software development in the new era in a short period of time, and how to make good use of software development models to better meet the needs of software development is still one of the urgent problems to be solved. From the perspective of software engineering, this paper deeply analyzes the advantages and existing problems of typical software development models, and analyzes the reasonable application of software development models in computer software development, which is for reference only. © 2022 ACM.

关键词： software reliability

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Fundamental characteristics and arrival curve modeling of live streaming traffic via 5G NR system based on OAI platform 5

Fundamental characteristics and arrival curve modeling of li...

引用

5th International conference on Artificial Intelligence and computer Science, AICS 2023

作者： Gao, Yuehong Kuerban, Alimu Wang, Xiaoqi Zhang, Xiaohang School of Information and Communication Engineerting Beijing University of Posts and Telecommunication Beijing100876 China

ISBN: (纸本)9781510668621

In order to better guarantee different quality of service requirements for different types of 5G network traffic, the characteristics should be studied and modeled for each type of traffic carried via 5G network. In this paper, we report a measurement-based study, which includes 5G network traffic data collection, analysis and modeling. To better extract the behavioral characteristics of 5G network traffic, we conduct the collection of traffic data from the 5G new radio (NR) system based on OpenAirInterface (OAI) platform and universal software radio peripheral (USRP). In this paper, three types of traffic carried via 5G network are collected and analyzed. For basic traffic characteristics, the traffic rates and the probability density distribution of packet length and packet interval are analyzed. For traffic modeling, the concept of cumulative arrival process is adopted and the arrival curve model is derived based on network calculus. © 2023 SPIE.

关键词： 5G mobile communication systems

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Image Defogging Algorithm Based on Physical Prior And Contrast Learning 5

Image Defogging Algorithm Based on Physical Prior And Contra...

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5th International conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Qu, Fengcheng Modern Education Technology Center Heihe University Heilongjiang China

ISBN: (纸本)9781839539848

Deep learning-based defogging methods can generate defogging results with high evaluation indexes end-to-end through the training of a large amount of data, but most of the existing methods do not make use of physical a priori with better defogging effects, and the use of synthetic data does not cope well with practical application scenarios due to the difficulty of obtaining supervised data, and the generalisation and robustness of the trained models are difficult to be guaranteed. therefore, to address the above problems, this paper incorporates physical a priori such as non-local, dark channel and bright channel into deep learning, and introduces the contrast learning method in self-supervised learning, which constrains the distance between the data by the extracted features, ensures the effective use of the real data, and improves the model's defogging quality and generalisability. In order to meet the high-quality requirements of computer vision systems for images, an interactive tool is designed through relevant software and calls the function of the haze removal algorithm designed in this paper to realise the functions of capturing real-time scene images and removing haze from images in different ways. © the Institution of engineering & Technology 2023.

关键词： Real time systems

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

引用

5th International conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

the utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. this issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. this framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. this paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Employee Fingerprint Identification Management System Based on Bidirectional ResNext Network with Triplet Loss 8

Employee Fingerprint Identification Management System Based ...

引用

8th International conference on Management engineering, software engineering and Service Sciences, ICMSS 2024

作者： Yang, Yuezhi Yang, Qian Su, Zhihao Wu, Wenlong Computer Science College Guangzhou College of Applied Science and Technology Guangzhou China La Consolacion University Philippines

ISBN: (纸本)9798350393705

With the increasing digitization and modernization of enterprise management, employee fingerprint identification management systems have become a key tool to ensure the safety and efficiency of the office space. Such a system not only accurately verifies the identity of employees but can also be used for attendance, access control and other related applications. However, improving the accuracy of fingerprint recognition remains a challenge. In this paper, we propose a fingerprint identification method based on a bidirectional ResNext network and combined with triplet loss. Compared with the traditional ResNext network, our method captures richer fingerprint features through bidirectional structure and triplet loss, thereby achieving higher matching accuracy. We conducted extensive experiments on the public FVC (Fingerprint Verification Competition) dataset and our own collected dataset CSFI (Company staff fingerprint identification). Experimental results show that compared with the original ResNext network, our method achieves approximately 3% and 5% performance improvements on the two data sets respectively. these results verify the effectiveness of our proposed method on fingerprint identification tasks and provide strong technical support for the further development of employee fingerprint identification management systems. © 2024 IEEE.

关键词： Access control

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Who Needs the Most Research Effort? Investigating the Importance of Smart Contract Weaknesses 5th

Who Needs the Most Research Effort? Investigating the Impo...

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5th International conference on Blockchain and Trustworthy systems, BlockSys 2023

作者： Chen, Shijian Wang, Yanlin Zheng, Zibin School of Software Engineering Sun Yat-sen University Zhuhai519000 China

ISBN: (纸本)9789819981007

Smart contracts have witnessed widespread adoption across various industries since the introduction of Ethereum. Also, smart contracts’ vulnerabilities have gradually attracted academic attention, e.g., Oyente was developed to detect security bugs in contracts. However, it remains unclear which smart contract vulnerabilities require more research attention. To fill the gap, we recruit eight researchers to collect 281 Ethereum-related security events from SlowMist and Rekt, and analyze the root causes (e.g., the code weakness) behind each occurrence based on SWC Registry. Our results reveal that only 10 SWC weaknesses lead to security events, causing a total loss of USD 74 Billion. Moreover, Reentrancy exhibits the highest frequency. Our results provide guidance for research efforts allocated in the academic community. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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