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International Conference on Advanced Aspects of Software engineering (ICAASE)

作者： Karboua Sabrina Fouzi Harrag Farid Meziane Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (数字)9798331522810

ISBN: (纸本)9798331522827

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models.

关键词： Translation Semantics Morphology Predictive models Encoding Multilingual Internet Recommender systems Software engineering Context modeling

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Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network

arXiv

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arXiv 2024年

作者： Huang, Zih-Syuan Lee, Ching-Pei Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Department of Advanced Data Science Institute of Statistical Mathematics Tachikawa Tokyo190-8562 Japan

We propose a Regularized Adaptive Momentum Dual Averaging (RAMDA) algorithm for training structured neural networks. Similar to existing regularized adaptive methods, the subproblem for computing the update direction of RAMDA involves a nonsmooth regularizer and a diagonal preconditioner, and therefore does not possess a closed-form solution in general. We thus also carefully devise an implementable inexactness condition that retains convergence guarantees similar to the exact versions, and propose a companion efficient solver for the subproblems of both RAMDA and existing methods to make them practically feasible. We leverage the theory of manifold identification in variational analysis to show that, even in the presence of such inexactness, the iterates of RAMDA attain the ideal structure induced by the regularizer at the stationary point of asymptotic convergence. This structure is locally optimal near the point of convergence, so RAMDA is guaranteed to obtain the best structure possible among all methods converging to the same point, making it the first regularized adaptive method outputting models that possess outstanding predictive performance while being (locally) optimally structured. Extensive numerical experiments in large-scale modern computer vision, language modeling, and speech tasks show that the proposed RAMDA is efficient and consistently outperforms state of the art for training structured neural network. Implementation of our algorithm is available at https://***/ismoptgroup/RAMDA/. © 2024, CC BY.

关键词： HTTP

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Multiplex measures for higher-order networks

arXiv

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arXiv 2024年

作者： Lotito, Quintino Francesco Montresor, Alberto Battiston, Federico Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Trento38123 Italy Department of Network and Data Science Central European University Vienna1100 Austria

A wide variety of complex systems are characterized by interactions of different types involving varying numbers of units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct types of higher-order interactions among a collection of units. In this work, we introduce a comprehensive set of measures to describe structural connectivity patterns in multiplex hypergraphs, considering scales from node and hyperedge levels to the system’s mesoscale. We validate our measures with three real-world datasets: scientific co-authorship in physics, movie collaborations, and high school interactions. This validation reveals new collaboration patterns, identifies trends within and across movie subfields, and provides insights into daily interaction dynamics. Our framework aims to offer a more nuanced characterization of real-world systems marked by both multiplex and higher-order interactions. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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A New Time Optimal Control based Tracking Differentiator with Small Phase Lag

A New Time Optimal Control based Tracking Differentiator wit...

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第43届中国控制会议

作者： Louyue Zhang Hehong Zhang Chao Zhai Xi Wang Zhihong Dan School of Energy and Power Engineering Beihang University College of Computer and Data Science Fuzhou University Science and Technology on Altitude Simulation Laboratory AECC Sichuan Gas Turbine Establishment Mianyang

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Real-time filtering and derivative signals with as small phase lag as possible are of great significance for control performances. In this work, an enhanced time optimal control(referred as f sa) based tracking differentiator(TD) is proposed,which can guarantee a small phase lag in both filtering and differential signals for a class of input signals with different levels of noises. This characteristic is achieved by introducing a damping coefficient to the boundary transformation function of the time-optimal control algorithm. Simulation results show that the proposed f sa based TD outperforms the fhan based TD in both filtering and differentiation.

关键词： tracking differentiator filtering differentiation phase lag system damping

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Risk estimation in a Markov cost process: lower and upper bounds 24

Risk estimation in a Markov cost process: lower and upper bo...

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Proceedings of the 41st International Conference on Machine Learning

作者： Gugan Thoppe Prashanth L. A. Sanjay P. Bhat Dept. of Computer Science and Automation Indian Institute of Science (IISc) Bengaluru India and Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ε-accuracy, either in expected or high-probability sense, requires at least Ω(1/ε2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs' mean. Even in that case, our lower bound of Ω(1/ε2) improves upon the existing Ω(1/ε) bound (Metelli et al., 2023).

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Research on video synopsis optimization method based on Two-stage abnormal behavior recognition 23

Research on video synopsis optimization method based on Two-...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Fudan Wang Zhixiang Zhu Chenwu Wang Pei Wang Lei Zhang Pengyuan Mu School of Computer Science Xi'an University of Posts and Telecommunications China Data Operations Department Shaanxi Information Engineering Research Institute China

ISBN: (纸本)9798400707674

Video synopsis techniques shift the moving target in the time axis to achieve the purpose of displaying as much effective information as possible in the shortest possible time, usually the abnormal behavior in the video contains more effective information, and most of the existing video synopsis methods do not consider the processing of abnormal behavior. To solve this problem, this paper proposes a video synopsis method based on two-stage abnormal behavior recognition. First, the abnormal frames containing abnormal behaviors in surveillance video are detected using a spatiotemporal autoencoder method, and then an improved R(2+1)D network model is used to identify the abnormal targets and abnormal behavior classes in the abnormal frames of surveillance video. Finally, the abnormal priority concentration loss term is added to the video synopsis optimization objective loss function to make the abnormal targets appear more preferentially and centrally in the generated synopsis video by the multi-objective optimization algorithm. Through a large number of experimental results, the method proposed in this article not only prioritizes the appearance of abnormal targets in the synopsis video, but also accurately observes the specific abnormal behavior of abnormal targets.

关键词： Anomaly detection Behavior recognition Tube rearrangement Video synopsis

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Leveraging Support Vector Machines for Detection of Network Traffic Attacks

Leveraging Support Vector Machines for Detection of Network ...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Anurag Tiwari School of Artificial Intelligence & Data Science Indian Institute of Technology Jodhpur India Department of Computer Science & Engineering Babu Banarasi Das Institute of Technology& Management Lucknow India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities.

关键词： Support vector machines Training Accuracy Scalability Intrusion detection Telecommunication traffic Network security Servers Kernel Testing

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Text Classification for Social Governance: A Novel Strategy with Adaptive Reward Mechanisms

Text Classification for Social Governance: A Novel Strategy ...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Xu Kong Xiaomin Zhu Maoli Wang Xinming Wang Min Zou School of Computer Science Qufu Normal University Rizhao China Shandong Institute of Big Data Jinan China School of Cyber Science and Engineering Qufu Normal University Qufu China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Given the increasing need for societal governance systems to effectively address public grievances, classifying texts from public service hotlines and other governance communications has become challenging, especially for texts with complex hierarchical structures. This study explores how adaptive reward mechanisms can be applied to classify complaint texts in societal governance. We propose a novel hierarchical text classification model that overcomes the limitations of traditional methods in handling hierarchical data. We designed a hierarchical loss function that considers inter-label hierarchical relationships to enhance the model's learning of hierarchical information. Additionally, we used a simple label encoding method to reduce computational complexity and improve training efficiency. Experimental results show that our model significantly outperforms existing methods in classification accuracy, both flat and hierarchical. Furthermore, ablation studies confirmed the hierarchical loss function's crucial role in enhancing model performance. This research offers an effective strategy for classifying complex hierarchical data, significantly improving classification tasks in natural language processing.

关键词： Training Support vector machines Adaptation models Visualization Accuracy Computational modeling Text categorization

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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Women in Computing (InCoWoCo), International Conference for

作者： Meena S Monisha Murthy Ashwini Kodipalli Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting; with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances.

关键词： Logistic regression Accuracy Machine learning algorithms Hospitals Computational modeling Mortality Predictive models Boosting Prediction algorithms data models

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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