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High-Speed Railway 2024年第2期2卷 101-109页

作者： Jiaxu Guo Ding Ding Peihan Yang Qi Zou Yaping Huang School of Computer Science and Technology Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Big Data&Artificial Intelligence in Transportation Ministry of EducationBeijing 100044China

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining ***,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale *** the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault *** algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent ***,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the *** experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

关键词： High utility Quantitative Frequent pattern mining Related degree pruning Fixed pattern length

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An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference

An Empirical Study on Cross-lingual Vocabulary Adaptation fo...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Yamaguchi, Atsuki Villavicencio, Aline Aletras, Nikolaos School of Computer Science University of Sheffield United Kingdom Department of Computer Science Institute of Data Science and Artificial Intelligence University of Exeter United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761681

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorates when generating text in languages other than English. This results in increased inference time and costs. Cross-lingual vocabulary adaptation (CVA) methods have been proposed for adapting models to a target language aiming to improve downstream performance. However, the effectiveness of these methods on increasing inference efficiency of generative LLMs has yet to be explored. In this paper, we perform an empirical study of five CVA methods on four generative LLMs (including monolingual and multilingual models) across four typologically-diverse languages and four natural language understanding tasks. We find that CVA substantially contributes to LLM inference speedups of up to 271.5%. We also show that adapting LLMs that have been pre-trained on more balanced multilingual data results in downstream performance comparable to the original models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Delay-Driven Iterative technology Mapping Framework

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第7期44卷 2585-2598页

作者： Liu, Junfeng Ni, Liwei Chen, Lei Li, Xing Zhao, Qinghua Li, Xingquan Ma, Shuai Pengcheng Laboratory Shenzhen China Beihang University School of Computer Science and Engineering Beijing China Huawei Noah's Ark Lab Hong Kong Hong Kong Hefei University School of Artificial Intelligence and Big Data Hefei China

technology mapping is the pivotal synthesis step that translates abstract logical models into technology-dependent implementations using the designated library, e.g., standard cells for ASICs. The efficient solutions heavily rely on the gate selection guided by estimated delay. However, estimating these delays is sophisticated due to the absence of actual interconnect load and transition time during the mapping. In this paper, we revisit the difficulties of the delay-driven mapping problem and explore three key insights to address these. Inspired by the insights, we first design a structure-Aware load-slew model that integrates input transitions and output loads for gate delay estimations. Benefiting from the model, we propose a delay-iterative framework that progressively reduces the overall circuit delay by further aligning library characteristics with logical network structures. Finally, experiments with 130nm and 7nm libraries show its superiority, which averagely reduces circuit delay by 10% with NLDM, and 6% in delay after P&R, as compared to ABC. © 2024 IEEE.

关键词： Delay circuits

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DNN Partition and Offloading Strategy with Improved Particle Swarm Genetic Algorithm in VEC

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-11页

作者： Li, Chunlin Chai, Long Jiang, Kun Zhang, Yong Liu, Jun Wan, Shaohua School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

Vehicular edge computing (VEC) is a novel computing paradigm, which is designed to satisfy the growing computation and communication needs of vehicle systems. With the assistance of VEC, vehicles can execute artificial intelligence (AI) tasks based on deep neural network (DNN), which are compute-intensive and delay-sensitive. However, it is difficult to deploy large-scale and compute-intensive DNN on resource-constrained terminal devices. Therefore, DNN model partition and offloading strategy have received a lot of attention, however, most of the researches have not taken into account the problem that the optimal partition point of a DNN model changes with the allocated computing resources. To address this problem, we propose a computing offloading strategy based on DNN model partition. This strategy selects the optimal DNN model partition points based on the computing capability of the vehicle, and then develops the optimal task offloading strategy to realize the effective distribution and execution of tasks between the edge server and the service vehicle. To minimize the task offloading delay, we propose an improved particle swarm genetic algorithm (IPSGA) to achieve the optimal offloading strategy. The algorithm uses the variable acceleration coefficient with the number of iterations and the inertia weight with the success rate as the feedback parameters to improve the particle swarm optimization algorithm (PSO), and the genetic algorithm (GA) is improved with the adaptive crossover probability and the adaptive mutation probability. Experimental results show that compared to the baselines, the IPSGA can reduce the overall system delay and increase the task completion rate. IEEE

关键词： Neural network models

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Enhancing Cancer Prediction: Investigating ACO-Based Feature Selection with Logistic Regression, XGBoost, and LightGBM 2

Enhancing Cancer Prediction: Investigating ACO-Based Feature...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Chidurala, Ashwini Basani, Shashanka Mulastam, Pranavi Nerella, Sanjay Srivastav, Shashank Dwivedi, Anshu Kumar SR University School of Computer Science and Artificial Intelligence Warangal India Buddha Institute of Technology Gida Department of Computer Science and Engineering Gorakhpur India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350368109

This report mainly concentrates on cancer prediction, particularly focusing on breast cancer prediction, which remains a significant challenge in healthcare. Improving patient outcomes requires early identification and machine learning approaches have enough opportunities in this area to improve the predicting capabilities. Feature selection plays a vital role in developing accurate predictive models, and this study investigates the effectiveness of Ant Colony Optimization (ACO) in enhancing feature selection for three commonly used machine learning algorithms: Logistic Regression, XGBoost, and LightGBM. The report begins with an introduction highlighting the importance of early cancer detection and the role of machine learning techniques in achieving the goal. Then presents a comprehensive literature review, summarizing previous research papers on cancer detection and classification using various machine learning approaches, with and without implementing ACO. The proposed methodology outlines the steps followed in the research process, from importing necessary libraries to evaluating the performance of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based Recognition

Can Large Language Models Grasp Event Signals? Exploring Pur...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yu, Zongyou Qu, Qiang Chen, Xiaoming Wang, Chen School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China School of Computer Science The University of Sydney Sydney Australia

ISBN: (纸本)9798350368741

Recent advancements in event-based zero-shot object recognition have demonstrated promising results. However, these methods heavily depend on extensive training and are inherently constrained by the characteristics of CLIP. To the best of our knowledge, this research is the first study to explore the understanding capabilities of large language models (LLMs) for event-based visual content. We demonstrate that LLMs can achieve event-based object recognition without additional training or fine-tuning in conjunction with CLIP, effectively enabling pure zero-shot event-based recognition. Particularly, we evaluate the ability of GPT-4o/4turbo and two other open-source LLMs to directly recognize event-based visual content. Extensive experiments are conducted across three benchmark datasets, systematically assessing the recognition accuracy of these models. The results show that LLMs, especially when enhanced with well-designed prompts, significantly improve event-based zero-shot recognition performance. Notably, GPT-4o outperforms the compared models and exceeds the recognition accuracy of state-of-the-art event-based zero-shot methods on N-ImageNet by five orders of magnitude. © 2025 IEEE.

关键词： event camera large language model object recognition

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Deep Reinforcement Learning Based Reliable data Transmission Scheme for Internet of Underwater Things in 5G and beyond Networks 2

Deep Reinforcement Learning Based Reliable Data Transmission...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India School of Computer Science and Artificial Intelligence SR University Anandsagar Hansaparthy Hanumakomda Telagana India

The Internet of Underwater Things (IoUT) is an interconnected communication ecosystem for underwater devices in maritime environments. IoUT devices range from seabed sensors to ships and boats in oceans which help in diverse practical applications of underwater information ranging from marine transportation, navigation, and underwater exploration to disaster prevention with intelligent monitoring. There exist significant obstacles within the present architecture of the IoUT. This article proposes three distinct methodologies aimed at enhancing the robustness and reliability of the communication architecture for 5G and future networks. Firstly, the employment of the Deep Reinforcement Learning approach is advocated for the identification of anomalies and faulty sensors. Secondly, energy consumption is identified as a crucial factor for IoUT devices. To address this concern, the integration of renewable energy sources, namely oxygen rechargeable batteries, with nano-generators is employed. This integration allows for the replenishment of the batteries through the harnessing of kinetic energy from water. Thirdly, the process by which information can be efficiently transmitted from IoUT sensors located on the seabed to the final terrestrial network is delineated, with an emphasis on minimizing the number of hops required. Additionally, a case study is presented, focusing on the mitigation of interference within the IoUT. Lastly, the article delves into the various research challenges and unresolved matters that persist within the realm of IoUT. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Crossover Operators between Multiple Scheduling Heuristics with Genetic Programming for Dynamic Flexible Job Shop Scheduling 13

Crossover Operators between Multiple Scheduling Heuristics w...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhu, Luyao Zhang, Fangfang Feng, Mengyuan Chen, Ke Zhu, Xiaodong Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798350308365

Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem that aims to optimise machine resources to improve production efficiency. Multi-tree genetic programming (MTGP) has been widely used to learn the routing rule and the sequencing rule for DFJSS simultaneously. Unlike traditional genetic programming that only operates crossover on a single tree, MTGP has various cases to conduct crossover since a genetic programming individual consists of more than one tree. Different crossover operators may affect the performance of MTGP for DFJSS. However, the investigation into different crossover operators in MTGP for DFJSS is rare. Specifically, it is not clear what influence will have on MTGP if involving both the routing rule and the sequencing rule for crossover. To this end, this paper provides a comprehensive investigation of four possible crossover cases between multiple scheduling heuristics with MTGP for DFJSS. The four operators are designed according to the number of trees/rules that crossover operator works on, and whether swapping full trees between parents. The results show that although the compared algorithms have comparable results in most scenarios, MTGP with both rules for crossover and the swapping strategy is ranked as the best one. Further analyses show that the sizes of learned rules are highly related to the crossover operators, and crossover involving more rules can increase the rule sizes, and vice versa. In addition, the population diversity and the number of unique features in the learned rules of MTGP with both rules for crossover are increased to learn effective rules. © 2024 IEEE.

关键词： Genetic programming

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