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Knowledge Management in Financial Software Support Team: A Prototype Based on Ontology 15th

Knowledge Management in Financial Software Support Team: A ...

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15th International Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Lazaretti, Maria Gabriela Costa Tenório, Nelson Teixeira, thaise Moser e Silva, Vitor Augusto Cecilio Becker, Steffi CESUMAR University Paraná Maringá Brazil State University of Maringá UEM Paraná Maringá Brazil Cesumar Institute of Scicence Technology and Innovation ICETI Paraná Maringá Brazil

ISBN: (纸本)9783031875687

In the software development companies, where knowledge is complex and shapes the development and maintenance of products, Knowledge Management (KM) efficiently presents a huge challenge. Tools designed for maintaining software products, such as bug tracking systems, primarily automate tasks without fully addressing the organization of knowledge. Incorporating an ontology to map out knowledge for easy retrieval, emerges as a promising strategy to enhance the effectiveness of these tools, mainly in times of artificial intelligence. this study introduces a prototype based on ontology addressed to improve KM within a software development company. throughout a field research approach in a company customer support department, this paper utilized data analysis and interactions with a key company representative to evaluate the prototype’s ability to tackle identified challenges. Our findings suggest that ontology is relevant as a viable tool for aiding to arrange the organizational knowledge, though further investigation is required to quantify the benefits of this solution in terms of investment return. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Development Maintenance Product line Retrieve

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GENIE Learn: Human-Centered Generative AI-Enhanced Smart Learning Environments 17

GENIE Learn: Human-Centered Generative AI-Enhanced Smart Lea...

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17th International conference on computer Supported education, CSEDU 2025

作者： Delgado Kloos, Carlos Asensio-Pérez, Juan I. Hernández-Leo, Davinia Manuel Moreno-Marcos, Pedro Bote-Lorenzo, Miguel L. Santos, Patricia Alario-Hoyos, Carlos Dimitriadis, Yannis Tabuenca, Bernardo Universidad Carlos III de Madrid Av. Universidad 30 Leganés Madrid28911 Spain Universidad de Valladolid Paseo de Belén 15 Valladolid47011 Spain Universitat Pompeu Fabra Roc Boronat 138 Barcelona08018 Spain Universidad Politécnica de Madrid Calle Alan Turing sn Madrid28031 Spain

ISBN: (纸本)9789897587467

this paper presents the basis of the GENIE Learn project, a coordinated three-year research project funded by the Spanish Research Agency. the main goal of GENIE Learn is to improve Smart Learning Environments (SLEs) for Hybrid Learning (HL) support by integrating Generative Artificial Intelligence (GenAI) tools in a way that is aligned with the preferences and values of human stakeholders. this article focuses on analyzing the problems of this research context, as well as the affordances that GenAI can bring to solve these problems, but considering also the risks and challenges associated with the use of GenAI in education. the paper also details the objectives, methodology, and work plan, and expected contributions of the project in this context. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Generative adversarial networks

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CKELM: Unified Approach to Parking Space Classification for All Weather Types 12th

CKELM: Unified Approach to Parking Space Classification for...

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12th International conference on Big Data and Artificial Intelligence, BDA 2024

作者： Navpreet Roul, Rajendra Kumar Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

ISBN: (纸本)9783031818202

Recently, surging urbanization and the subsequent increase in vehicular movement have amplified the popularity of parking management systems. A crucial hurdle in effective parking management is the optimal utilization of available parking spaces. Although deep learning techniques have showcased their prowess in accurately identifying parking spaces, they sometimes falter when partial obstructions and diverse lighting conditions are present. this challenge is further exacerbated during foggy or hazy weather, resulting in a decline in their performance. this research article introduces a novel approach that leverages the benefits of CNN and Kernel Extreme Learning Machine (KELM). the approach not only enhances accuracy but also significantly reduces training time. the key innovation lies in replacing the fully connected layer of CNN with KELM, which circumvents the backpropagation process and reduces the training time. A purpose-built data set tailored to classify parking spaces in hazy conditions and PKLot is employed for the validation. Within the proposed approach, images from the hazy dataset undergo preprocessing using Light-DehazeNet (LD-Net) to counteract the effects of haze. Subsequently, CNN is trained on this preprocessed dataset to extract features and generate a feature vector. this feature vector is input into the KELM to classify parking spaces as occupied or vacant. Compared with standalone CNN, KELM, machine learning classifiers, and similar existing models, the proposed model showcases superior performance and better computational efficiency. this study demonstrates that the integration of CNN with KELM offers a promising solution for parking space classification in challenging weather conditions. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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AI-Driven Nitrogen Stress Management in Cereal Crops via Drone technology 18th

AI-Driven Nitrogen Stress Management in Cereal Crops via D...

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18th International KES conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Yimer, Hailemicael Lulseged Cristani, Matteo Workneh, Tewabe Chekole Tomazzoli, Claudio Department of Computer Science University of Verona Strada le Grazie 15 I Verona37134 Italy

ISBN: (纸本)9789819764686

this paper addresses the global challenge of food production losses caused by plant diseases, pests, and nitrogen stress, focusing on the specific context of Ethiopia where cereal crop yields face a significant annual decline of 20–30%, i.e. losses of 420000 tons per year. Traditional fertilization methods have proven imprecise and inefficient. To tackle this issue, the study proposes an artificial intelligence-based system for early detection, analysis, and treatment of nitrogen stress in cereal crops, particularly corn. the integrated system combines Android applications and drone technology. the system demonstrates robust performance metrics, achieving a mean average precision exceeding 60. the model is described as secure, user-friendly, and reliable, making it suitable for testing in diverse African scenarios. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Behavioral research

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. this paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Oracle Bone Script Recognition Based on Multi-scale Feature Fusion and Knowledge Distillation 27th

Oracle Bone Script Recognition Based on Multi-scale Feature ...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Wang, Jiaoyan Xu, Xuebin Aysa, Alimjan Ding, Jun Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Ürümqi830046 China

ISBN: (纸本)9783031783111

Oracle bone scripts, being the oldest and most developed writing system discovered in China till date, have been meticulously studied by numerous scholars. this paper presents the Multi–scale Feature Fusion Attention Net (MFFA–Net) as a solution to the issue of variant characters in the oracle bone affecting recognition accuracy. the network is based on ResNet18 and employs asymmetric convolution alongside a refined coordinate attention technique to acquire image features. It also integrates perceptual field information of varying sizes through hierarchical bilinear pooling. Furthermore, knowledge from the Wide_ResNet101 and DenseNet169 is transferred to MFFA-Net using knowledge distillation techniques. Finally, to validate the effectiveness of our proposed method, we conducted rigorous experiments on the OBC306, OBC265, and EOBC datasets, comparing our results with those of existing methods. the experimental results demonstrate that our method obtains remarkable performance in oracle character recognition, reaching state-of-the-art Top–1 accuracy with 92.42%, 94.78%, and 98.82% on the OBC306, OBC265, and EOBC datasets, respectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attention Feature Fusion Knowledge Distillation Oracle Recognition

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Oracle Character Recognition Based on Attention Enhancement and Multi-level Feature Fusion 27th

Oracle Character Recognition Based on Attention Enhancement...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Han, Zhiwang Yadikar, Nurbiya Xu, Xuebin Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Urumqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031781186

Oracle bone characters represent the earliest inscriptions in China. Recognizing and deciphering these characters is significant. Despite some progress made by recent methods, their recognition accuracy remains limited by two major issues: 1) how to focus on characters features within complex background noise images, and 2) how to effectively fuse shallow detail information with deep semantic information. To address these issues, we propose a novel deep learning model called Character Feature Enhancement Network (CFE-Net). the model consists of two key components: Character Feature Enhancement (CFE) and Adaptive Multi-level Classifier Fusion (AMCF). Specifically, CFE utilizes the Spatial Focus Attention Module (SFAM) to focus on extracting foreground character features and suppressing background noise, thereby significantly enhancing high-level semantic representation capabilities. AMCF, on the other hand, achieves multi-level feature fusion by adaptively fusing the outputs of different classifiers, effectively avoiding information loss or interference that simple fusion strategies might cause. We evaluated the CFE-Net on two rubbing oracle bone characters benchmark datasets, OBC306 and Oracle-MNIST. the experimental results demonstrate that CFE-Net significantly outperforms several existing methods in terms of Top-1 accuracy, establishing it as the new state-of-the-art. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Stable NICE Model-Based Picture Generation for Generative Steganography 6th

Stable NICE Model-Based Picture Generation for Generative S...

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6th International conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Cui, Xutong Zhou, Zhili Yang, Jianhua Yuan, Chengsheng Tang, Weixuan Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Institute of Artificial Intelligence Guangzhou University Guangdong510006 China School of Cyber Security Guangdong Polytechnic Normal University Guangdong510006 China

ISBN: (纸本)9783031736988

Steganography is one of most important techniques for covert communication. In recent years, generative steganography, which transforms a secret information into a generated picture, is a prospective steganography-resistant technique. Nevertheless, it is difficult to achieve a good trade-off between information hiding ability and extraction accuracy because of the low efficiency and irreversibility of the secret-to-picture conversion. In order to solve this problem, this paper proposes a secret message-driven picture generation solution for generative steganography. the presented SM-IG scheme is founded on the design of a stable version of the Nearly Independent Component Estimation (Stable NICE) model, allowing for a stable bijection mapping between a potential space with simple distributions and an picture space with complex distributions. During the secret to picture conversion, a latent vector is constructed, driven by a given secret message, which is then mapped to the generated picture via the Stable NICE modelAs a result, the secret information is eventually converted into the generated picture. Due to the good efficiency and reversibility of the SM-IG scheme, this steganography method has high hiding capability and accurate message extraction accuracy. the experiments prove that the proposed SM-IG can simultaneously realise good-level hiding capacity (as much as 4 bpp) and precise extraction accuracy (close to 100% accuracy) without compromising the required resistance to detection and imperceptibility. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Digital forensics Generative steganography Information hiding Steganography

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An Encoder-Based Framework for Privacy-Preserving Machine Learning 24th

An Encoder-Based Framework for Privacy-Preserving Machine L...

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24th International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Jiayun Ren, Wei Zhang, Xianchao Zheng, Xianghan School of Computer Science China University of Geosciences Wuhan430074 China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China Ministry of Education Chengdu China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China College of Computer and Big Data Fuzhou University Fujian Fuzhou China School of Information and Intelligent Engineering Sanya College Hainan China

ISBN: (纸本)9789819615506

As a data-driven science, machine learning requires vast amounts of training data and computational resources. However, for highly privacy-sensitive data, it is crucial to protect the privacy of the data during both the training and utilization of machine learning models. In this paper, we propose a privacy-preserving machine learning approach using autoencoders and differential privacy mechanisms to safeguard data privacy while minimizing the impact on data availability. Specifically, we augment logistic regression and ResNet18 models with different architectures of autoencoders to perform data encryption? without compromising the machine learning tasks. Additionally, we employ differential privacy mechanisms to introduce gradient perturbations in the encoding part of the autoencoder, enhancing the algorithm’s security and further protecting data privacy. We also design the cosine similarity between the encoded and original data as a metric for evaluating data privacy, considering model performance, privacy budget, and data privacy collectively to balance data availability and privacy. Extensive experiments conducted on MNIST, CIFAR-10, PathMNIST, and BloodMNIST datasets demonstrate that for simple logistic regression models handling easily classifiable datasets, employing simple autoencoder structures can enhance classification accuracy, with significant performance impact after adding differential privacy. For ResNet18, utilizing convolutional autoencoders for data encryption generally has minimal impact on model classification performance and can even improve accuracy in most cases. Adding differential privacy has minor effects on model classification performance. Selecting appropriate model structures and privacy budgets for different usage scenarios can ensure both data availability and privacy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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OSOL: Open Linked Data Generation Using Semantics Oriented Learning Paradigms 15th

OSOL: Open Linked Data Generation Using Semantics Oriented L...

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15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is an imminent need for a strategic framework for open linked data and meta tag generation for web pages as they would be useful in indexing which would serve as a roadmap for retrieval and also several other applications such as text summarization, text mining, etc. this paper proposes an open-linked data generation framework in which the XGBoost classifier classifies the web URL dataset. Category extraction and URL canonicalization are performed which helps in strategic maximal utilization of the term set. Strategic knowledge base repositories like CYC and NELL are used for entity enrichment in the model. the RBFNN classifies the web-based directories extracted from the current structure of the web using a focused crawler. Unlike most traditional frameworks, the OSOL uses Semantics-oriented reasoning, which is achieved through the SimRank measure with an empirically decided threshold. the Eagle optimization algorithm (a metaheuristic, nature-inspired algorithm) generates the optimal solution set. Shannon’s entropy helps in feature selection for classifying the dataset. this framework can achieve an overall mean precision percentage of 95.17%, a mean recall percentage of 96.75%, and an FDR value of 0.05, making it the best-in-class model for linked open data and meta-tag generation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： entity enrichment linked open data meta tag generation retrieval web-based directories

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