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arXiv 2024年

作者： Guo, Shengnan Wei, Tonglong Huang, Yiheng Zhao, Miaomiao Chen, Ran Lin, Yan Lin, Youfang Wan, Huaiyu School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Department of Computer Science Aalborg University Aalborg Denmark

Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services. Despite significant advancements in this field, selecting the most suitable model for practical applications remains challenging due to three key issues: 1) incomprehensive consideration of missing patterns that describe how data loss along spatial and temporal dimensions, 2) the lack of test on standardized datasets, and 3) insufficient evaluations. To this end, we first propose practice-oriented taxonomies for missing patterns and imputation models, systematically identifying all possible forms of real-world traffic data loss and analyzing the characteristics of existing models. Furthermore, we introduce a unified benchmarking pipeline to comprehensively evaluate 10 representative models across various missing patterns and rates. This work aims to provide a holistic understanding of traffic data imputation research and serve as a practical guideline. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models

arXiv

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arXiv 2023年

作者： Aghdam, Saeideh Niksirat Hossayni, Sayyed Ali Sadeh, Erfan Khedersolh Khozouei, Nasim Bidgoli, Behrouz Minaei Department of Computer Engineering Iran University of Science and Technology Tehran Iran Data Mining Lab School of Computer Engineering Iran University of Science & Technology Tehran Iran Department of computer Engineering Faculty of Engineering Yasouj University Yasouj Iran

Semantic role labeling (SRL) is the process of detecting the predicate-argument structure of each predicate in a sentence. SRL plays a crucial role as a pre-processing step in many NLP applications such as topic and concept extraction, question answering, summarization, machine translation, sentiment analysis, and text mining. Recently, in many languages, unified SRL dragged lots of attention due to its outstanding performance, which is the result of overcoming the error propagation problem. However, regarding the Persian language, all previous works have focused on traditional methods of SRL leading to a drop in accuracy and imposing expensive feature extraction steps in terms of financial resources, time and energy consumption. In this work, we present an end-to-end SRL method that not only eliminates the need for feature extraction but also outperforms existing methods in facing new samples in practical situations. The proposed method does not employ any auxiliary features and shows more than 16 (83.16) percent improvement in accuracy against previous methods in similar circumstances. © 2023, CC BY-NC-ND.

关键词： Deep neural networks

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Design and Implementation of a BERT Based Weibo Public Opinion Monitoring System 14th

Design and Implementation of a BERT Based Weibo Public Opini...

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14th International Conference on computer engineering and Networks, CENet 2024

作者： Chen, Shun-Chao Zhu, Tao Pang, Zi-An Xu, Chen Department of Computer Information and Cyber Security Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Jiangsu Police Institute Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province NAN Jing210031 China

ISBN: (纸本)9789819642281

With the rapid development of online We Media, it is not easy to monitor and guide massive public opinion information. The massive forwarding, emotional polarization, and mixed rumors of blog posts are highly likely to cause online public opinion events. A Weibo public opinion monitoring system based on bidirectional encoder representation from transformers (BERT) is proposed to support the full lifecycle guidance for public opinion. After Weibo text data is crawled from blog posts of important informants according to the lifecycle stages, and preprocessed for data cleaning and text vectorization, an emotional tendency classification and rumor detection model with the way of ensemble learning is proposed with the latent Dirichlet allocation (LDA) model and three BERT-based combining model. For extracting emotional tendency information efficiently, the LDA model is applied to extract topics on emotional polarity in blog posts. In order to address the issue of long text data caused by the lifting of restrictions on short posts on Weibo, three BERT-based model are ensembled together to achieve better performance. The experiment test demonstrates that the evolution of online public opinion can be described efficiently by this system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BERT LDA public opinion monitoring We Media

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Effects of Early Update Cost in Cloud Object Storage Scheduling 23rd

Effects of Early Update Cost in Cloud Object Storage Schedul...

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23rd International Conference on Network-Based Information Systems, NBiS 2020

作者： Sukhvibul, Titipat Natwichai, Juggapong Data Engineering Laboratory Department of Computer Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9783030578107

Object storage, a type of cloud storage as a service, allows organizations to have highly available storage with zero infrastructure investments and thus fewer operation costs. Usually, a fixed-selection of storage tier on the cloud service is not appropriate since the workload on the objects is not static, and each storage tier cost can be drastically different from another. To efficiently select the appropriate object storage tiers, tedious work is usually required for planning, which could be modeled as the job shop scheduling problem with a pre-calculated threshold. To the best of our knowledge, no solution considers an early update cost, a penalty cost when the objects are moved out to another tier or deleted before an agreed stored-time is reached. In this paper, we present the effect of the early update cost of the existing cloud object scheduling algorithms, particularly when objects in a cold tier are read heavily, or spike read. From the experiments, it is found that the performance of the algorithms can be enormously worse when the EUC is applied. © 2021, Springer Nature Switzerland AG.

关键词： Scheduling algorithms

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Using Machine Learning with Pyspark for Solving a Big data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

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7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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GLaPE: Gold Label-agnostic Prompt Evaluation for Large Language Models

GLaPE: Gold Label-agnostic Prompt Evaluation for Large Langu...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Xuanchang Zhang, Zhuosheng Zhao, Hai College of Zhiyuan Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761643

Despite the rapid progress of large language models (LLMs), their task performance remains sensitive to prompt design. Recent studies have explored leveraging the LLM itself as an optimizer to identify optimal prompts that maximize task accuracy. However, when evaluating prompts, such approaches heavily rely on elusive manually annotated gold labels to calculate task accuracy for each candidate prompt, which hinders its generality. To overcome the limitation, this work proposes GLaPE, a gold label-agnostic prompt evaluation method to alleviate dependence on gold labels. GLaPE is composed of two critical aspects: self-consistency evaluation of a single prompt and mutual-consistency refinement across multiple prompts. Experimental results on 8 widely-recognized reasoning tasks demonstrate that GLaPE can produce more effective prompts, achieving performance comparable to those derived from manually annotated gold labels. Analysis shows that GLaPE provides reliable evaluations aligned with accuracy, even in the absence of gold labels. Code is publicly available at https://***/thunderous77/GLaPE. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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A Hybrid Marine Predator Algorithm for Thermal-aware Routing Scheme in Wireless Body Area Networks

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Journal of Bionic engineering 2023年第1期20卷 81-104页

作者： Tao Hai Jincheng Zhou Mohammad Masdari Haydar Abdulameer Marhoon School of Computer and Information Qiannan Normal University for NationalitiesDuyun558000GuizhouChina Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000GuizhouChina Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Universiti Teknologi MARA40450Shah AlamSelangorMalaysia Department of Computer Engineering Islamic Azad UniversityUrmia BranchUrmiaIran Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq

Thermal-aware routing protocols in WBANs consider temperature factors in the routing process for preventing overheating of the tissues surrounding the sensor ***,providing an energy-efficient and thermal-aware routing in WBANs is a challenging *** deal with this problem,this article presents a novel temperature-aware routing protocol that applies Mamdani-based Fuzzy Logic Controllers(FLCs)for selecting the next forwarding node in routing data *** FLCs apply five important input factors such as the priority of the packet,and sensor node's remaining energy,temperature,distance,and link path ***,a new hybrid version of the Marine Predator Algorithm(MPA),named MPAOA is presented by combining the exploration and exploitation phases of the MPA and Arithmetic Optimization Algorithm(AOA).This algorithm is effectively applied for selecting the best possible set of fuzzy rules for FLCs and tuning their fuzzy *** experiments conducted in the Castalia simulator exhibit that the proposed temperature and priority-aware routing scheme can outperform other well-known routing schemes such as LATOR,TTRP,TAEO,ATAR,and EOCC-TARA in terms of metrics such as sensor nodes lifetime,the average temperature of the sensor nodes,and the percentage of the packets routed through non-overheated ***,it is shown that the MPAOA outperforms other algorithms such as Bat Algorithm(BA),Genetic Algorithm(GA),AOA,and MPA regarding the specified metrics.

关键词： WBANs Multi-hop routing Temperature Priority Fuzzy logic Bio-inspiration

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