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FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

IEEE Internet of Things Journal 2025年第13期12卷 23257-23274页

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Variational Information Bottleneck with Gaussian Processes for Time-Series Classification 23

Variational Information Bottleneck with Gaussian Processes f...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Efrati, Itamar Fine, Shai School of Computer science Reichman University Herzliya Israel The Data Science Institute Reichman University Herzliya Israel

ISBN: (纸本)9798350374889

Time series classification problems are prevalent across various domains, often characterized by intra-series relationships within features, and inter-series relationships between the same features over time. Developing a generalized model capable of capturing these intricate properties poses a considerable challenge. In this paper, we introduce an innovative approach termed Gaussian Process Variational Information Bottleneck (GP-VIB). This model is designed as an end-to-end system, with a primary focus on acquiring a concise representation of the initial sequence. It aims to retain essential information vital for accurate classification. Through our experiments, we illustrate that the proposed GP-VIB model outperforms existing methods on renowned benchmark datasets. © 2024 IEEE.

关键词： Time series

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Offline Behavior Distillation 38

Offline Behavior Distillation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lei, Shiye Zhang, Sen Tao, Dacheng School of Computer Science The University of Sydney Australia College of Computing & Data Science Nanyang Technological University Singapore

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity O(1/(1 − γ)2). We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity O(1/(1 − γ)). Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. The code is available at https://***/LeavesLei/OBD. © 2024 Neural information processing systems foundation. All rights reserved.

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Understanding Open Source Large Language Models: An Exploratory Study 2

Understanding Open Source Large Language Models: An Explorat...

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2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Sowe, Sulayman Mou, Yongli Cheng, Du Kong, Lingxiao Neumann, Alexander Tobias Decker, Stefan RWTH Aachen University Computer Science 5 Aachen Germany Data Science and AI Sankt Augustin Germany

ISBN: (纸本)9798350354799

Prompted by the increasing dominance of proprietary Large Language Models (LLMs), such as OpenAI's GPT-4 and Google's Gemini, concerns about data privacy, accessibility and bias have led to a growing advocacy for OSLLMs. This study investigates Open Source Large Language Models (OSLLMs), exploring their characteristics, openness, and community interactions. Our research aims to define OSLLMs (license, openness, community engagement). Utilizing data from the Hugging Face platform, we examine the popularity metrics, license distribution, artefact accessibilities and community engagement of LLM projects. Findings reveal a skewed distribution of model usage, with a few models dominating downloads and likes. Apache 2.0 and MIT are the most common licenses among top models, highlighting a preference for flexible usage terms. However, a significant portion of models lack specified licenses, posing potential legal challenges. Openness analysis shows that nearly half of the examined models share their training code and datasets, with standardized evaluation metrics common across repositories. Community engagement analysis indicates that engineer users are more active than general users, contributing significantly to technical discussions. Sentiment analysis of forum interactions reveals varying user attitudes, with licensed models generally receiving more positive feedback. This study underscores the potential of OSLLMs to democratize AI access and foster innovation, while also highlighting areas for improvement in community engagement and model openness. © 2024 IEEE.

关键词： Sentiment analysis

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Weighted Probability Density Estimator with Updated Bandwidths

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Journal of Systems science & Complexity 2024年第2期37卷 886-906页

作者： WU Yi YU Wei WANG Xuejun PRAKASA RAO B L S School of Big Data and Artificial Intelligence Chizhou UniversityChizhou 247000China School of Big Data and Statistics Anhui UniversityHefei 230601China CR Rao Advanced Institute of Mathematics Statistics and Computer ScienceHyderabad 500046India

In this paper,the authors study a class of weighted version of probability density *** is shown that the weighted estimator contains some existing estimators of probability density,no matter they are recursive or *** statistical results including weak consistency,strong consistency,rate of strong consistency,and asymptotic normality are established under some mild ***,the random weighted estimator is also *** numerical simulations and a real data analysis are presented to study the numerical performances of the estimators.

关键词： Asymptotic normality consistency numerical simulation probability density real data analysis updated bandwidths weighted estimator

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Toward Improved Knowledge Retention: A Template for Describing data science Projects

Toward Improved Knowledge Retention: A Template for Describi...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Haertel, Christian Staegemann, Daniel Pohl, Matthias Daase, Christian Turowski, Klaus Otto-von-Guericke-University Faculty of Computer Science Magdeburg Germany Institute of Data Science Jena Germany

ISBN: (纸本)9798350362480

data science (DS) aims to extract knowledge from large amounts of data. Organizations can use the retrieved insights to achieve various performance improvements. However, DS projects often fail to fulfill their objectives due to the explorative nature of this discipline and technical as well as managerial challenges. Consequently, new approaches to support DS project execution are sought after. A viable contribution in this regard is improving knowledge retention in DS to predict socio-technical obstacles of an undertaking and derive best practices. Therefore, in this work, a template for describing the central characteristics of DS projects is proposed using a Design science Research approach. The artifact is structured based on the common DS project stages and features 32 fields, enabling comparability and transparency in DS. The applicability of the template is demonstrated based on three DS use cases from the literature. While further steps for evaluation are pending, the template can serve as a foundation for developing a categorization model for DS projects in the future. © 2024 IEEE.

关键词： data science

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CyberGuardian: An Interactive Assistant for Cybersecurity Specialists Using Large Language Models

CyberGuardian: An Interactive Assistant for Cybersecurity Sp...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587061

Cybersecurity plays an important role in protecting people and critical infrastructure. Sectors such as energy, defense and healthcare are increasingly at risk from cyber threats. To address these challenges, dedicated Security Operations Centers (SOCs) continuously monitor threats and respond to critical issues. Our research focuses on the use of Large Language Models (LLMs) to optimize the tasks of SOCs and to support security professionals. In this work, we propose a framework, which we call CyberGuardian, whose main goal is to provide a chatbot application along with a set of tools to support SOC analysts in real-time cybersecurity tasks. We use state-of-the-art LLM techniques and start from a Llama 2 model, then fine-tune the base model using a new dataset containing mainly cybersecurity topics. The CyberGuardian framework has a plugin architecture that integrates processes such as Retrieval Augmented Generation (RAG), safeguard methods for interaction between human user and chatbot, integration with tools to manage tasks such as database interactions, code generation and execution, and plotting graphs just by specifying the task in a natural language. The work, along with the dataset we collected and reusable code to update or customize, is made available to the cybersecurity community as open source. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Critical infrastructures

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Enhance Transferability of Adversarial Examples with Model Architecture 48

Enhance Transferability of Adversarial Examples with Model A...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Fan, Mingyuan Guo, Wenzhong Ying, Zuobin Liu, Ximeng Fuzhou University College of Computer and Data Science China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781728163277

Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical setting, the crafted adversarial examples are always prone to overfitting to the proxy model employed, presenting poor transferability. In this paper, we suggest alleviating the overfitting issue from a novel perspective, i.e., designing a fitted model architecture. Specifically, delving the bottom of the cause of poor transferability, we arguably decompose and reconstruct the existing model architecture into an effective model architecture, namely multi-track model architecture (MMA). The adversarial examples crafted on the MMA can maximumly relieve the effect of model-specified features to it and toward the vulnerable directions adopted by diverse architectures. Extensive experimental evaluation demonstrates that the transferability of adversarial examples based on the MMA significantly surpass other state-of-the-art model architectures by up to 40% with comparable overhead. © 2023 IEEE.

关键词： Architecture

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Enhancing Automated COVID Diagnosis from Chest X-rays using Convolutional Neural Networks and Transfer Learning 5

Enhancing Automated COVID Diagnosis from Chest X-rays using ...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Bukhari, Muhammad Abdullah Shah Siddiqui, Muhammad Ahmad Khalique, Sarmad Bukhari, Faisal Iqbal, Waheed University of the Punjab Department of Computer Science Lahore Pakistan University of the Punjab Department of Data Science Lahore Pakistan

ISBN: (纸本)9798350394764

We propose an approach for the early detection of COVID-19 and other related lung diseases using artificial intelligence (AI) and deep learning-based methods. The proposed approach involves utilizing transfer learning over convolutional neural networks (CNNs) for the classification of chest X-ray images as normal or COVID-19 positive. To address the limited availability of X-ray images, we employed data augmentation techniques to expand the dataset size and enhance our model's performance on unseen data. We used variations of four pre-trained models: DenseNet, Inception, ResNet, and VGG, and found that DenseNet169 demonstrated the best performance with a validation accuracy of 100%. © 2024 IEEE.

关键词： COVID-19

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