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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Iterative Methods via Locally Evolving Set Process 38

Iterative Methods via Locally Evolving Set Process

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Baojian Sun, Yifan Harikandeh, Reza Babanezhad Guo, Xingzhi Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science Stony Brook University United States Samsung SAIT AI Lab

Given the damping factor α and precision tolerance ϵ, Andersen et al. [2] introduced Approximate Personalized PageRank (APPR), the de facto local method for approximating the PPR vector, with runtime bounded by Θ(1/(αϵ)) independent of the graph size. Recently, Fountoulakis & Yang [12] asked whether faster local algorithms could be developed using Õ(1/(√αϵ)) operations. By noticing that APPR is a local variant of Gauss-Seidel, this paper explores the question of whether standard iterative solvers can be effectively localized. We propose to use the locally evolving set process, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized. Let vol(St) and γt be the running average of volume and the residual ratio of active nodes St during the process. We show vol(St)/γt ≤ 1/ϵ and prove APPR admits a new runtime bound Õ(vol(St)/(αγt)) mirroring the actual performance. Furthermore, when the geometric mean of residual reduction is Θ(√α), then there exists c ∈ (0, 2) such that the local Chebyshev method has runtime Õ(vol(St)/(√α(2 − c))) without the monotonicity assumption. Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference 41

Improving Group Robustness on Spurious Correlation Requires ...

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41st International Conference on Machine Learning, ICML 2024

作者： Han, Yujin Zou, Difan Department of Computer Science The University of Hong Kong Hong Kong Department of Computer Science Institute of Data Science The University of Hong Kong Hong Kong

Standard empirical risk minimization (ERM) models may prioritize learning spurious correlations between spurious features and true labels, leading to poor accuracy on groups where these correlations do not hold. Mitigating this issue often requires expensive spurious attribute (group) labels or relies on trained ERM models to infer group labels when group information is unavailable. However, the significant performance gap in worst-group accuracy between using pseudo group labels and using oracle group labels inspires us to consider further improving group robustness through preciser group inference. Therefore, we propose GIC, a novel method that accurately infers group labels, resulting in improved worst-group performance. GIC trains a spurious attribute classifier based on two key properties of spurious correlations: (1) high correlation between spurious attributes and true labels, and (2) variability in this correlation between datasets with different group distributions. Empirical studies on multiple datasets demonstrate the effectiveness of GIC in inferring group labels, and combining GIC with various downstream invariant learning methods improves worst-group accuracy, showcasing its powerful flexibility. Additionally, through analyzing the misclassifications in GIC, we identify an interesting phenomenon called semantic consistency, which may contribute to better decoupling the association between spurious attributes and labels, thereby mitigating spurious correlation. The code for GIC is available at https://***/yujinhanml/GIC. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Roughness Analysis of Stainless Steel Bearing Using computer Vision 6

Roughness Analysis of Stainless Steel Bearing Using Computer...

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6th International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2024

作者： Das, Shounak Tarafdar, Arundhati Mondal, Prabir Computer Science and Engineering Jadavpur University Kolkata India School of Computer Science Data Science cluster UPES University Dehradun India Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9798350380040

Current technology utilizes a sophisticated standard room measurement system to analyze the average roughness of Stainless Steel Bearing Surface. However, this process relies on random sampling at infrequent intervals, resulting in inefficiency and inadequate analysis of a large number of samples. To address this issue, we propose a solution using computer vision via laser scanning. The scanning process produces an amplified speckle pattern of the ring surface. This pattern is then analyzed to verify the number of edges that cross a predefined threshold and exhibit symmetrical patterns, particularly on smoothed ring surfaces. Remarkably, this system operates with a turnaround time of less than a second. Moreover, unlike the standard room setup, this technology can be seamlessly integrated directly into production/manufacturing units. © 2024 IEEE.

关键词： Speckle

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SMT-PREDICT: An Efficient Framework for Stock Market Trend Prediction using Historical and Sentimental data 3

SMT-PREDICT: An Efficient Framework for Stock Market Trend P...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Raju, Abhijith Arun, K. Sudha, S.K. Aji, S. University of Kerala Department of Computer Science Kerala India University of Kerala Centre for Data Science and Information Processing Department of Computer Science Kerala India

ISBN: (纸本)9798350354171

Predicting stock market trends is vital due to its economic impact and benefits. In global finance, stock movements are influenced by numerous factors, especially news and sentiment. High volatility, driven by political, economic, leadership changes, and investor sentiment, makes stock prices hard to predict. Solely relying on historical data or textual information for predictions has proven insufficient. Hence, this paper proposes an efficient framework using Sentiment Polarity Inscribed Deep Features (SPIDF). Deep features are extracted from historical stock data using a Denoising Autoencoder (DAE), while the Sentiment Polarity Index (SPI) is derived from news headlines to generate SPIDF. The robust SPIDF is then used to predict stock market trends (SMT-Predict) with a Bi-Long Short-Term Memory (BiLSTM) network. The results demonstrate that the SMT-Predict model effectively incorporates SPIDF with essential Financial Indicators (FI) for accurate stock market trend prediction. © 2024 IEEE.

关键词： Marketplaces

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Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And database Schema With Pseudo-Schema 31

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Shi, Jie Xu, Bo Liang, Jiaqing Xiao, Yanghua Chen, Jia Xie, Chenhao Wang, Peng Wang, Wei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Computer Science and Technology Donghua University China School of Data Science Fudan University China SenseDeal Intelligent Technology Co. Ltd. China

ISBN: (纸本)9798891761964

With the prevalence of Large Language Models (LLMs), recent studies have shifted paradigms and leveraged LLMs to tackle the challenging task of Text-to-SQL. Because of the complexity of real world databases, previous works adopt the retrieve-then-generate framework to retrieve relevant database schema and then to generate the SQL query. However, efficient embedding-based retriever suffers from lower retrieval accuracy, and more accurate LLM-based retriever is far more expensive to use, which hinders their applicability for broader applications. To overcome this issue, this paper proposes Gen-SQL, a novel generate-ground-regenerate framework, where we exploit prior knowledge from the LLM to enhance embedding-based retriever and reduce cost. Experiments on several datasets are conducted to demonstrate the effectiveness and scalability of our proposed method. We release our code and data at https://***/jieshi10/gensql. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of computer science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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Features selection and prediction for IoT attacks

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High-Confidence Computing 2022年第2期2卷 1-6页

作者： Jingyi Su Shan He Yan Wu Data Science BGSUUnited States Computer Science BGSUUnited States

Cyber-attacks and anomaly detection are growing concerns in the Internet of Things(IoT).With fast-growing deployment and opportunities,an increasing number of attacks put IoT devices under the threat of continuous exploitation and *** operation,denial of service,MITM,and scan are major types of attacks that can cause IoT devices to *** study how a variety of machine-learning algorithms,such as decision tree,random forest,and gradient-boosting machine(GBM)analyze and predict network attacks on IoT *** comparing performance indicators for various algorithms through different model evaluations,we conclude that a decision-tree algorithm is generally the most accurate compared with random forest and gradient-boosting machine,but the random forest algorithm has better AUC scores as it combines the results of multiple individual decision ***-boosting machine performs well,but from an accuracy and time aspect,it may not be the best option.

关键词： IoT Machine learning Anomaly detection

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Ordinal classification for knee osteoarthritis x-rays using vision transformers

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Multimedia Tools and Applications 2025年 1-28页

作者： Tariq, Tayyaba Suhail, Zobia Nawaz, Zubair Department of Computer Science University of the Punjab Allama Iqbal Campus Punjab Lahore54000 Pakistan Department of Data Science University of the Punjab Allama Iqbal Campus Punjab Lahore54000 Pakistan

Knee Osteoarthritis is a progressive and chronic knee joint disease that shows symptoms of pain, stiffness, and swelling and is diagnosed by knee radiographs. These radiographs are evaluated by radiologists using Kellgren and Lawrence’s (KL) grading scheme. According to this scheme, the knee radiograph is graded from KL grade 0 (i.e., Normal) to KL grade 4 (i.e., Severe) to assess the KOA severity. For an automated system, the first step is to localize knee joints from knee radiographs and then classify them according to the KL grading system. In this work, we have proposed vision-based transformers to localize knee joints. We also proposed a single-stage model to localize the knee and classify KOA using a single model. We also explored and compared the performance of various transformers using a multiclass image classification strategy. Since KL grades maintain intrinsic ordinal information of KL grades, we employed an ordinal classification-based strategy to improve automated knee osteoarthritis classification performance. A total of 10 models are trained and validated on multiple datasets, and visualizations are generated for the best model for better comprehension. Our model obtained state-of-the-art results for knee detection and ordinal X-ray classification for KOA. Analysis of Variance (ANOVA) tests also indicate that the ordinal classification approach significantly improves the performance of models over traditional image classification approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Arthroplasty

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