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4th International Conference on computer science and Educational Informatization, CSEI 2022

作者： You, Jinguo Wang, Ziwen Zhao, Xiaoxia Kou, Weili Zhang, Yunwei Zhang, Ting Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Computer Center Kunming University of Science and Technology Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Kunming China School of Information Engineering Jiaozuo University Henan Jiaozuo China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Teaching mode design is a process to achieve the learning goal. Under the background of information technology, online and offline hybrid teaching mode has become the new form of education, and the closely related teaching mode design plays an essential role in the success of teaching implementation. Scenario case teaching can stimulate students' interest in learning and improve their ability to analyze and solve problems. Taking the MySQL database course as an example, this paper introduces the design and implementation process of online and offline hybrid teaching modes based on scenario cases. The practical results demonstrate that the hybrid teaching mode far outperforms the traditional teaching effect in terms of the aspects of students' basic knowledge of databases, practical operation level, and self-learning ability. Furthermore, this teaching mode has gained popularity in the studying of both our university and league universities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Design

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Lu Jing Zhao Lichao Ding Zenghao Hao 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 Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied 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: (数字)9781665410205

ISBN: (纸本)9781665410212

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method.

关键词： Training Uncertainty Soft sensors Noise Training data Knowledge graphs data models Stability analysis Reliability Thermal stability

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Can Lexicon-Based Sentiment Analysis Boost Performances of Transformer-Based Models?

Can Lexicon-Based Sentiment Analysis Boost Performances of T...

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Conference on New Media Studies (CoNMedia)

作者： Lindung Parningotan Manik Harry Susianto Arawinda Dinakaramani Niken Pramanik Totok Suhardijanto Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Department of Computer Science University of Nusa Mandiri Jakarta Indonesia Faculty of Psychology University of Indonesia Depok Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia Department of Linguistics Faculty of Humanities University of Indonesia Depok Indonesia

An essential endeavor in natural language processing, sentiment analysis entails determining the sentiment expressed in a text. Transformer-based models like BERT have attained state-of-the-art performance in sentiment analysis tasks. However, these algorithms may have difficulty distinguishing sentiment-laden words. In response, we proposed combining lexicon-based sentiment analysis and transformer-based models. This study investigates the effect of lexicon-based sentiment analysis, particularly SentiStrength, on BERT's efficacy in sentiment analysis tasks. Experimental evaluations reveal that incorporating sentiment lexicons enhances the accuracy and F1-score of classical sentiment analysis compared to the baseline BERT model. Our findings demonstrate the value of incorporating external knowledge sources into transformer-based sentiment analysis models.

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Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI

arXiv

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arXiv 2024年

作者： Godau, Patrick Srivastava, Akriti Adler, Tim Maier-Hein, Lena NCT Heidelberg DKFZ University Hospital Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Karlsruhe Germany Medical Faculty Heidelberg University Germany

The field of medical imaging AI is currently undergoing rapid transformations, with methodical research increasingly translated into clinical practice. Despite these successes, research suffers from knowledge silos, hindering collaboration and progress: Existing knowledge is scattered across publications and many details remain unpublished, while privacy regulations restrict data sharing. In the spirit of democratizing of AI, we propose a framework for secure knowledge transfer in the field of medical image analysis. The key to our approach is dataset"fingerprints", structured representations of feature distributions, that enable quantification of task similarity. We tested our approach across 71 distinct tasks and 12 medical imaging modalities by transferring neural architectures, pretraining, augmentation policies, and multi-task learning. According to comprehensive analyses, our method outperforms traditional methods for identifying relevant knowledge and facilitates collaborative model training. Our framework fosters the democratization of AI in medical imaging and could become a valuable tool for promoting faster scientific advancement. © 2024, CC BY.

关键词： Multi-task learning

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Roheet Bhatnagar Esraa Hassan Abeer Saber Mahmoud Y. Shams Samar Elbedwehy Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Information Technology Dept. Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Dept. of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%.

关键词： computer vision Accuracy Measles Feature extraction Transformers Skin Lesions Artificial intelligence Viruses (medical) Testing

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Competitive Facility Location under Random Utilities and Routing Constraints

arXiv

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arXiv 2024年

作者： Pham, Hoang Giang Dam, Tien Thanh Duong, Ngan Ha Mai, Tien Hà, Minh Hoàng ORLab Faculty of Computer Science Phenikaa University Ha Dong Hanoi Viet Nam ORLab-SLSCM Faculty of Data Science and Artificial Intelligence National Economics University Hai Ba Trung Hanoi Viet Nam School of Computing and Information Systems Singapore Management University 80 Stamford Rd 178902 Singapore Department of Engineering Computer Science and Mathematics University of L'Aquila AQ L'Aquila67100 Italy

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Location

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Decoding the Workplace & EOR: An Employee Survey Analysis by data science Techniques and Visualization

arXiv

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arXiv 2023年

作者： Bhimani, Kishankumar Saradva, Khushbu Laboratory on AI for Computational Biology Department of Data Analysis and Artificial Intelligence Faculty of Computer Science HSE University 11 Pokrovsky Boulevard Moscow109028 Russia

This research study explores the new dynamics of employee-organization relationships (EOR) [6] using advanced data science methodologies and presents findings through accessible visualizations. Leveraging a dataset procured from a comprehensive nationwide big employee survey, this study employs innovative strategy for theoretical researcher by using our state-of-the-art visualization. The results present insightful visualizations encapsulating demographic analysis, workforce satisfaction, work environment scrutiny, and the employee's view via word cloud interpretations and burnout predictions. The study underscores the profound implications of data science across various management sectors, enhancing understanding of workplace dynamics and promoting mutual growth and satisfaction. This multifaceted approach caters to a diverse array of readers, from researchers in sociology and management to firms seeking detailed understanding of their workforce's satisfaction, emphasizing on practicality and interpretability. The research encourages proactive measures to improve workplace environments, boost employee satisfaction, and foster healthier, more productive organizations. It serves as a resourceful tool for those committed to these objectives, manifesting the transformative potential of data science in driving insightful narratives about workplace dynamics and employee-organization relationships. In essence, this research unearths valuable insights to aid management, HR professionals, and companies in harnessing data science techniques for their future research endeavors. © 2023, CC BY-NC-ND.

关键词： data visualization

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TransModE: Translational Knowledge Graph Embedding Using Modular Arithmetic

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Procedia computer science 2022年 207卷 1154-1163页

作者： Hussein Baalbaki Hussein Hazimeh Hassan Harb Rafael Angarita Institut Supérieur d'Electronique de Paris LISITE 75006 Paris France Data Science department Faculty of Information Lebanese University Beirut Lebanon Computer Science department Faculty of Sciences Lebanese University Beirut Lebanon

In order to improve link prediction process in a knowledge graph, we tackle the problem of learning representations of entities and relations. The strength of the existing models in this domain mainly relies on its ability of modeling and inferring the patterns of the relations. In this paper, we propose a new knowledge graph embedding approach called TransModE that has the ability to represent all simple and complex relation patterns. Inspired by TransE, TransModE represents the relations between the entities of a knowledge graph as a transition in the modulus space. Experimenting our model on multiple benchmark knowledge graphs shows the simplicity and the scalability of TransModE. It also shows that TransModE outperforms existing state-of-the-art models by being able to infer and model all types of relations.

关键词： Knowledge Graph Embedding Knowledge Graphs Link Prediction Reasoning

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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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