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A Blockchain-Based Intelligent Framework for Secure and Sustainable Knowledge Management in Organizational Systems

Procedia computer science 2025年 258卷 2899-2908页

作者： Kajal Singh Anand Singh Rajawat S.B. Goyal Hayyan Nassar Waked Business Analyst Barclays Bank 400 Jefferson Park Whippany NJ 07981 USA School of Computer Science & Engineering Sandip University Nashik Maharashtra India Faculty of Information Technology City University Petaling Jaya 46100 Malaysia City Graduate School City University Petaling Jaya 46100 Malaysia

The modern-day organizational system requires secure and constant knowledge management to work efficiently in the in a competitive age—blockchain-based Intelligent Framework for Securing and Managing Knowledge Management Process Lifecycle within Organizations. Utilizing blockchain capabilities and integrated with artificial intelligence (AI), the system combines a comprehensive solution to manage knowledge, including security. Blockchain secures data integrity and transparency via decentralized ledgers, smart contracts, and consensus algorithms — tokenizing their B2B economic relationships, while AI allows the system to analyze knowledge flows better, predict, and optimize. The proposed framework uses blockchain to allow storage and verification of knowledge transactions, resulting in an immutable versioning system that logs all touches. AI algorithms, especially machine learning models, are used in knowledge mining techniques to scrutinize the patterns of using acquaintance, from which it may predict what we need and can be disseminated at the right time. The integration provides the ability for real-time monitoring and management of knowledge assets to help you keep your information accessible in a way that is both quick and can be done without risk from attackers. Experimental results show that the proposed framework performed well in terms of secure data management, high prediction accuracy, and recommendation. AI-powered Kasistos set the stage to drive knowledge from siloes, and blockchain ensures every piece of newly-permission wisdom into its walls is securely lodged: all verified by it. In such a process, we will ensure that organizational knowledge is retained, which helps in the long run to ensure resources are being used effectively and redundancy is minimized. Blockchain-Based Intelligent and Innovative Framework provides a complete platform for the current complexity of knowledge management obstacles with advanced technologies blending to secure organiz

关键词： Blockchain Artificial Intelligence Knowledge Management Data Security Predictive Analytics Organizational Systems

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Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction

arXiv

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arXiv 2025年

作者： Zhang, Quan Qi, Yuxin Tang, Xi Yuan, Rui Lin, Xi Zhang, Ke Yuan, Chun Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Pseudo-label learning methods have been widely applied in weakly-supervised temporal action localization. Existing works directly utilize weakly-supervised base model to generate instance-level pseudo-labels for training the fully-supervised detection head. We argue that the noise in pseudo-labels would interfere with the learning of fully-supervised detection head, leading to significant performance leakage. Issues with noisy labels include:(1) inaccurate boundary localization;(2) undetected short action clips;(3) multiple adjacent segments incorrectly detected as one segment. To target these issues, we introduce a two-stage noisy label learning strategy to harness every potential useful signal in noisy labels. First, we propose a frame-level pseudo-label generation model with a context-aware denoising algorithm to refine the boundaries. Second, we introduce an online-revised teacher-student framework with a missing instance compensation module and an ambiguous instance correction module to solve the short-action-missing and many-to-one problems. Besides, we apply a high-quality pseudo-label mining loss in our online-revised teacher-student framework to add different weights to the noisy labels to train more effectively. Our model outperforms the previous state-of-the-art method in detection accuracy and inference speed greatly upon the THUMOS14 and ActivityNet v1.2 benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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A Blockchain-Integrated AI Framework for Enhancing Energy Efficiency and Sustainability in Smart Grids

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Procedia computer science 2025年 258卷 2302-2311页

作者： Kajal Singh S.B. Goyal Anand Singh Rajawat Hayyan Nassar Waked Business Analyst Barclays Bank 400 Jefferson Park Whippany NJ 07981 USA Faculty of Information Technology City University Petaling Jaya 46100 Malaysia School of Computer Science & Engineering Sandip University Nashik Maharashtra India City Graduate School City University Petaling Jaya 46100 Malaysia

In the pursuit of energy efficiency and sustainability in smart grids, leveraging blockchain integration with artificial intelligence could provide a disruptive paradigm that offers numerous benefits. In this paper, a new framework is presented using blockchain with AI in order to provide optimal energy management and sustainable smart grid. The new model combines blockchain, which provides a secure an unchangeable log of transactions; with algorithms based on AI in order to analyse and predict patterns of electricity consumption as well as provide demand response control (the ability needed by users who want to be active prosumers) accompanied by optimization of the energy delivery chain. Blockchain enforces transparency and trust among multiple stakeholders, it helps verify data integrity across entities involved in a transaction to prevent any kind of tampering or fraud. At the same time, AI-driven insights allow real-time data to inform energy systems and lead with scheduling dynamic changes in order to prevent grid losses that would result in wasted energy. The inclusion of these technologies is what helps to solve the major problems such as energy theft, data security and also reduce inefficient use of power. Indeed, this integration helps reduce operational costs and enables a shift towards cleaner and sustainable sources of energy. Simulations and real-world use cases are performed to evaluate the effectiveness of the proposed framework in improving energy efficiency for smart grid towards more sustainability.

关键词： Blockchain Technology Artificial Intelligence (AI) Smart Grids Energy Efficiency Sustainability Decentralized Systems

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An Analysis for Pre-consultations on the Korean Government-Managed information System Projects 8th

An Analysis for Pre-consultations on the Korean Government-M...

引用

8th International Conference on Advances in Computing and Data sciences, ICACDS 2024

作者： Kim, Myunghee Baek, Nakhoon Department of Digital Platform Government National Information Society Agency Daegu41068 Korea Republic of Department of Information Science Kyungpook National University Daegu41566 Korea Republic of School of Computer Science and Engineering Kyungpook National University Daegu41566 Korea Republic of Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of

ISBN: (纸本)9783031709050

To reduce duplicated information system development projects, Korean government established the pre-consultation body. The pre-consultation body performs preliminary review for various aspects of government-managed information systems. In spite of its good roles and its effectively, the pre-consultation body met over-loaded duties, and now it shows some warning signals mainly due to the increased number of submitted projects. This paper statistically analyzes the results of pre-consultation processes for 1,399 submitted projects in the field of e-Government projects in the year of 2022. Our analysis shows that it is better to exclude some small-size projects from the pre-consultation process. This paper aims to provide the theoretical and statistics-based backgrounds to the claims, and we expect that our suggestions can improve the pre-consultation process of Korean government information systems management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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A Near-Optimal Category information Sampling in RFID Systems

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading 30th

Efficient Compute Resource Sharing of RISC-V Packed-SIMD Us...

引用

30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Takata, Shogo Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan

ISBN: (纸本)9783031856372

AI tasks are gaining popularity in the area of IoT and edge devices. To run such tasks on devices, QNNs are used because of their reduced size and ability to be computed with simple integer arithmetic. There have been many implementations to support such a network format. However, when considering thread-level parallelism to speedup the program, many often implement a multi-core architecture or clusters which needs to copy all resources for each core. In this paper, we introduce a new RISC-V Out-of-Order Simultaneous Multi-Threading core "B4SMT" with RISC-V Packed-SIMD extension for evaluation. We also show that even a single executor could increase the performance of a 1D median filter by over 100×, and a matrix multiplication by over 30 on more than 16 threads efficiently. Furthermore, we suggest that other infrequently used executors may be placed as a shared resource efficiently in an SMT core. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Median filters

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

引用

arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

arXiv

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arXiv 2025年

作者： Cao, Yang Yang, Sikun Li, Chen Xiang, Haolong Qi, Lianyong Liu, Bo Li, Rongsheng Liu, Ming School of Computing and Information Technology Great Bay University China Great Bay Institute for Advanced Study Great Bay University China Graduate School of Informatics Nagoya University Japan School of Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum [East China China College of Cyberspace Security Zhengzhou University China School of Computer Harbin Engineering University China School of IT Deakin University Australia

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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Blockchain-based Edge Computing Service with Dynamic Entry and Exit Mechanism

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IEEE Transactions on Mobile Computing 2025年

作者： He, Qiang Feng, Zheng Fang, Hui Wang, Xingwei Zhao, Liang Yu, Keping Choo, Kim-Kwang Raymond Northeastern University College of Medicine and Biological Information Engineering China Shanghai University of Finance and Economics Research Institute for Interdisciplinary Science School of Information Management and Engineering China Northeastern University College of Computer Science and Engineering Shenyang 110169 China Shenyang Aerospace University College of Computer Science and Engineering Shenyang 110136 China Hosei University Graduate School of Science and Engineering Tokyo 184-8584 Japan University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio 78249 TX United States

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency. © 2002-2012 IEEE.

关键词： Deep reinforcement learning Edge computing Entry and exit mechanism Internet of Things networks

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

引用

2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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