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

作者： Tu, Hanqian Xiang, Dongming Lin, Wang Liu, Guanjun Zhejiang Sci-Tech University Department of Computer Science and Technology Hangzhou310018 China Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Shanghai201804 China

ISBN: (纸本)9781665410205

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method. © 2024 IEEE.

关键词： Information leakage

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Combination of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods in Pythagorean m -Polar Fuzzy Environment: A Case Study of Sign Language Recognition Systems

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International Journal of Information Technology and Decision Making 2023年第4期22卷 1341-1369页

作者： Albahri, O.S. Alsattar, H.A. Garfan, Salem Qahtan, Sarah Zaidan, A.A. Ahmaro, Ibraheem Y. Y. Alamoodi, A.H. Zaidan, B.B. Albahri, A.S. Al-Samarraay, Mohammed S. Jasim, Ali Najm Baqer, M.J. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia Department of Computer Center Middle Technical University's Baghdad Iraq Faculty of Engineering & It British University in Dubia Dubai United Arab Emirates Computer Science Department College of Information Technology Hebron University Hebron Palestine Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Baghdad Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq

In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, wherea

关键词： Decision making

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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

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computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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Augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education 2nd

Augmented Reality Immersive World with Hologram Special Effe...

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2nd International Conference on science, Engineering Management and Information Technology, SEMIT 2023

作者： Astriani, Maria Seraphina Bahana, Raymond Ahmad, Arif Priyono Susilo Kurniawan, Andreas Yi, Lee Huey Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta11480 Indonesia Creative Advertising Program Visual Communication Design Department School of Design Bina Nusantara University Jakarta11480 Indonesia Water Lily Melaka75200 Malaysia

ISBN: (纸本)9783031722837

Early childhood education is a critical stage of education because it may establish the foundation for an individual’s intelligence. Students of this age group are generally unable to remain seated and study for long periods of time because they get bored easily. They are more likely to be attracted to visual information and enjoy interactive activities. This condition challenges the world of education to keep developing learning methods that are able to help students to learn the materials. The combination of Augmented Reality technology and hologram special effects is a promising solution because it allows students to see an immersive world and allows them to interact with real-life physical objects while learning. Augmented Reality immersive world solution with hologram special effects in early childhood education has successfully been tested and implemented by using two smartphones, one acts as an input-camera and the other acts as an output to display the hologram. This solution is specially designed to make it easier for students to learn the materials without using wearable devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Holograms

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Optimizing Mobile Cloud computing: A Comparative Analysis and Innovative Cost-Efficient Partitioning Model

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SN computer science 2025年第1期6卷 1-25页

作者： Ali, Mushtaq Muhammad, Dost Khalaf, Osamah Ibrahim Habib, Raja Faculty of Computing Riphah International University Islamabad Pakistan CRT-AI and ADAPT Research Centers School of Computer Science University of Galway Galway Ireland Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Iraq Department of Computing Shifa Tameer-e-Millat University Islamabad Pakistan

Mobile cloud computing (MCC) integrates mobile devices with cloud computing to enhance performance and provide scalable services. It enables the partitioning of computationally intensive mobile applications, utilizing the cloud resources for remote execution. This process is vital for optimizing resource utilization and energy efficiency in distributed computing environments. Initially, static application partitioning was developed, dividing system resources into fixed-sized partitions. Though it is simple and reduces operational overhead, it lacks adaptability to dynamic computing environments due to mobility. Modern frameworks use runtime partitioning, dynamically allocating resources based on runtime profiling, which increases computational overhead and energy consumption. Despite various studies on MCC, there is a notable oversight in numerical analysis of application partitioning. This paper presents a comparative numerical study of existing partitioning techniques and proposes a Cost-Efficient Partitioning (CEP) model. The CEP model combines the static nature to reduce computational overhead with the dynamic nature to address runtime challenges. This research addresses the shortcomings of existing approaches, potentially transforming computational offloading into energy-saving solutions. Our results show that the execution time (ET) of a task in the CEP model is 0.826 s, which is longer than the ET of static partitioning however shorter than the 0.962 s of dynamic partitioning models. Similarly, the CEP's energy consumption in a test scenario is 0.609 J, slightly higher than the 0.587 J of static partitioning but lower than the 0.711 J of dynamic partitioning. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational offloading Dynamic partitioning Energy consumption Execution cost Mobile computing Remote execution Static partitioning

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How Augmented Reality Works: Technical Issues, User Issues, Content Development, and Curriculum Adaptations as Challenges Through Bibliometric Analysis

How Augmented Reality Works: Technical Issues, User Issues, ...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Zamahsari, Gamal Kusuma Nur Amalia, Mufidah Rifah, Lailatul Permana, Frihandhika Faculty of Humanities Bina Nusantara University Digital Language Learning Center Communication Science Department Jakarta Indonesia Faculty of Humanities Bina Nusantara University Digital Language Learning Center Computer Science Department Jakarta Indonesia Faculty of Humanities Bina Nusantara University Digital Language Learning Center Visual Communication Design Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Augmented reality has been effectively used in the domain of education. Nevertheless, the development of technology is not without its problems, particularly when it comes to aligning it with existing demands and prevailing trends. The primary objective of this research endeavor is to provide a comprehensive account and identify pertinent concerns pertaining to the challenges encountered in the use of augmented reality (AR), particularly within the realm of education. A bibliometric study was performed using analytical tools available on the *** website, and the Vos viewer application was used for the viewing of bibliometric data. The study's results indicate that there are many concerns that stakeholders should take into consideration while using augmented reality in educational settings. There are four distinct hurdles in the adaptation of Augmented Reality (AR) technology to meet the demands of learning environments. These challenges include technical issues, user-related concerns, the production of AR content, and the adaptation of curricula to effectively incorporate AR into educational settings. © 2023 IEEE.

关键词： Augmented reality

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Ishimizu, Yusei Li, Jialong Yamauchi, Takuto Chen, Sinan Cai, Jinyu Hirano, Takanori Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798331530839

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions. © 2024 IEEE.

关键词： Control system synthesis

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Land Classification Plugin for QGIS Using Pix2Pix

Land Classification Plugin for QGIS Using Pix2Pix

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2024 Symposium on Insight to Foresight via Geospatial Technologies

作者： Cubillas, James Earl Torculas, Reynard Mayol, Jessie Bajao, Paul Justine Virtual Learning Research Center Caraga State University Butuan City Philippines Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines

Land cover classification is critical in various fields, including environmental monitoring, urban planning, and ecological assessment, facilitating informed decision-making processes. Traditional land cover classification methods often involve labor-intensive and time-consuming processes, relying on manual intervention and predefined algorithms. The emergence of deep learning techniques, particularly convolutional neural networks (CNNs), offers a promising solution to automate this process, albeit with complexities in implementation. This study addresses the limitations of existing Geographic Information System (GIS) software and plugins by proposing a novel approach utilizing the Pix2Pix architecture, a type of CNN, for automated land cover classification. The proposed Land Classification Plugin (LCP) integrates seamlessly with QGIS, offering an end-to-end solution for generating classified static maps. The methodology involves preprocessing data, utilizing the Pix2Pix model for image segmentation, and post-processing to produce georeferenced outputs. The development of the LCP involved extensive software and hardware configurations, including essential components like GDAL/OGR, PyTorch, and OpenCV. The plugin's architecture comprises a user-friendly interface for region selection, clipping, and classification aided by the Pix2Pix model. A layout manager feature also allows for the creation of composite maps for enhanced visualization. The accuracy assessment of the LCP demonstrated an overall accuracy of 83.40% across diverse land cover classes, indicating its efficacy in classification tasks. The plugin's capabilities offer significant potential for applications in land management, environmental surveillance, and urban planning, revolutionizing current practices in land cover classification within the realm of GIS software. © Author(s) 2024.

关键词： Convolutional neural networks

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Circinus: An AI-Based Technical Description Plotting

Circinus: An AI-Based Technical Description Plotting

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2024 Symposium on Insight to Foresight via Geospatial Technologies

作者： Cubillas, James Earl Daguil, Rolyn Virtual Learning Research Center Caraga State University Butuan City Philippines Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines

The digitization of maps poses a significant challenge for GIS operators and engineers, particularly in encoding bearings and distances into GIS software. This study, titled "Circinus: An AI-Based Technical Description Plotting," represents a substantial advancement in the digitization process of land records. Leveraging artificial intelligence, this application enhances the efficiency of digitizing land survey maps, potentially surpassing the laborious manual input required for geographic information. By expediting the encoding process of scanned maps, the application enhances the accessibility and availability of digital land records. This advancement holds considerable benefits for effective land asset planning and improved financial operations, particularly benefiting prospective homebuyers and landowners. The tool offers streamlined encoding through its semi-automated features, facilitating ease of use. Additionally, its optimization features have the potential to significantly enhance the productivity of engineers and GIS operators involved in encoding scanned land survey maps. © Author(s) 2024.

关键词： Convolutional neural networks

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Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning 38

Provably Transformers Harness Multi-Concept Word Semantics f...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bu, Dake Huang, Wei Han, Andi Nitanda, Atsushi Suzuki, Taiji Zhang, Qingfu Wong, Hau-San Department of Computer Science City University of Hong Kong Hong Kong Center for Advanced Intelligence Project RIKEN Japan Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Mathematical Informatics The University of Tokyo Japan

Transformer-based large language models (LLMs) have displayed remarkable creative prowess and emergence capabilities. Existing empirical studies have revealed a strong connection between these LLMs' impressive emergence abilities and their in-context learning (ICL) capacity, allowing them to solve new tasks using only task-specific prompts without further fine-tuning. On the other hand, existing empirical and theoretical studies also show that there is a linear regularity of the multi-concept encoded semantic representation behind transformer-based LLMs. However, existing theoretical work fail to build up an understanding of the connection between this regularity and the innovative power of ICL. Additionally, prior work often focuses on simplified, unrealistic scenarios involving linear transformers or unrealistic loss functions, and they achieve only linear or sub-linear convergence rates. In contrast, this work provides a fine-grained mathematical analysis to show how transformers leverage the multi-concept semantics of words to enable powerful ICL and excellent out-of-distribution ICL abilities, offering insights into how transformers innovate solutions for certain unseen tasks encoded with multiple cross-concept semantics. Inspired by empirical studies on the linear latent geometry of LLMs, the analysis is based on a concept-based low-noise sparse coding prompt model. Leveraging advanced techniques, this work showcases the exponential 0-1 loss convergence over the highly non-convex training dynamics, which pioneeringly incorporates the challenges of softmax self-attention, ReLU-activated MLPs, and cross-entropy loss. Empirical simulations corroborate the theoretical findings. © 2024 Neural information processing systems foundation. All rights reserved.

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