检索结果-内蒙古大学图书馆

Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

International Conference on Green Energy, computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Text Recognition for Station Equipment Troubleshooting Based on BERT-BiLSTM-CRF Models

Text Recognition for Station Equipment Troubleshooting Based...

引用

2023 China Automation Congress, CAC 2023

作者： Bai, Wei Wang, Xiaosu Liang, Bo Yang, Guoyuan Zhi, Hanyu Qiao, Yinwei Institute of Computing Technology China Academy of Railway Sciences Co. Ltd. Beijing China Postgraduate Department China Academy of Railway Sciences Beijing China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350303759

The excessive length and high semantic complexity of station equipment fault handling report texts are addressed to the extent that effective text recognition is difficult to achieve. A station equipment fault handling dataset is formulated and a text recognition system for station fault reports based on the BERT-BiLSTM-CRF model is designed. The method first preprocesses the data using the BERT pre-trained language model to generate word vectors, which are then fed into BiLSTM to obtain the embedded contextual information, which is passed through the CRF model to obtain the global optimal solution, and finally outputs the textual results of the station equipment fault handling. The experimental results show that the model outperforms the other five comparative models by more than 2% in terms of precision, recall, and F1 value (checking accuracy) for intelligent recognition over the five entities predicted by the other five comparative models. This brings a new direction of thinking for the analysis of equipment troubleshooting texts in railway stations. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

引用

Computer Modeling in engineering & Sciences 2022年第4期131卷 23-26页

作者： Qi Liu Xiaodong Liu Radu Grosu Ching-Nung Yang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburgh018373UK Institute of Computer Engineering Vienna University of TechnologyWien1040Austria Department of Computer Science and Information Engineering National Dong Hwa UniversityHualien97401Taiwan

Cyber Physical Systems(CPS)have been appealing in recent years as a result of the rapid emergence of unique hardware and software compositions that create smart,autonomously behaving ***-to-end procedures and new types of user-machine interaction are made possible via the *** the one hand,these CPS applications have the potential to deliver crucial services in a variety of developing application areas,including energy management,healthcare,traffic monitoring,industrial assessment and surveillance.

关键词： CPS hardware services

来源：评论

学校读者我要写书评

暂无评论

Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets

arXiv

引用

arXiv 2023年

作者： Koufakou, Anna Garciga, Jairo Paul, Adam Morelli, Joseph Frank, Christopher Department of Computing and Software Engineering Florida Gulf Coast University Fort MyersFL United States

Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experimental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled. © 2023, CC0.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A Blockchain based Framework for Verifying Drug Authenticity

A Blockchain based Framework for Verifying Drug Authenticity

引用

Information Technology (ACIT)

作者： Soomaiya Hamid Narmeen Zakaria Bawany Kiran Fatima Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Karachi Pakistan

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Feasibility Study of Fitting the Normal Distribution and Gamma Distribution to Rainfall Data at Kuantan River Basin 5th

A Feasibility Study of Fitting the Normal Distribution and G...

引用

5th International Conference on Water Resources, ICWR 2021

作者： Ghani, Nadiatul Adilah Ahmad Abdul Senawi, Azlyna Subramaniam, Roshan Department of Civil Engineering College Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Kuantan26300 Malaysia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia

ISBN: (纸本)9789811959462

Rainfall data can be very useful for decision makers in developing precautionary strategies to mitigate the impact of climate change in a variety of ways, such as forecasting floods. Prior to the data being utilised, it is critical to determine the best fit probability distribution of the data. Only after this has been done then it is possible to obtain reliable analysis and results. In this study, the feasibility of fitting the data with normal distribution was investigated. The investigation specifically used peak monthly values of rainfall data from ten hydrological stations of the Kuantan River Basin during the year 1975 to 2017. Four different test statistics were employed to assess the possibility of fitting the data to the normal and Gamma distributions: Skewness, excess kurtosis, Kolmogorov–Smirnov and Shapiro–Wilk. Results obtained show that normal distribution fits well with the annual daily maximum rainfall data for only two hydrological stations, but normal distribution is not a good fit for the seasonal daily maximum rainfall case for all stations involved. Gamma 1P distribution was found to be not an ideal fit for both annual and seasonal data for all stations, but Gamma 2P, on the contrary, was discovered to fit well for all stations except one station. The findings of the study are anticipated to improve rainfall forecasting in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

来源：评论

学校读者我要写书评

暂无评论

Proactive Hybrid Autoscaling for Container-Based Edge Applications in Kubernetes 19th

Proactive Hybrid Autoscaling for Container-Based Edge Appli...

引用

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Zhu, Kaile Shen, Shihao Lan, Shizhan Wang, Xiaofei Zhang, Cheng Qiu, Chao Leung, Victor College of Intelligence and Computing Tianjin University Tianjin China China Mobile Guangxi Institute Co. Ltd. Shanghai China Institute of Technology Tianjin University of Finance and Economics Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9783031651229

As the rising of the Internet of Things (IoT), edge computing is widely adopted in numerous applications. However, current autoscaling tools are not designed for edge applications and can not utilize the heterogeneous resources of edge nodes efficiently. In this paper, we propose a proactive hybrid autoscaler specifically optimized for edge computing scenario. With the Bidirectional Long Short Term Memory (Bi-LSTM) based load prediction model, the proposed autoscaler is able to predict the future workload and perform scaling operation before it arrives. In addition, a overload compensation algorithm is implemented to mitigate the Quality of Service (QoS) decreasing due to under-prediction. Then, a hybrid scaling method is applied to simultaneously modify the number of pods and their resource quotas without restarting. Experimental results with a real-world workload dataset shows the proposed load prediction model has better accuracy compared with the Long Short Term Memory model and the state-of-the-art statistical analysis model, Autoregressive Integrated Moving Average (ARIMA), which is also more than 350 times slower than our model in prediction speed. Finally, evaluation in a real Kubernetes cluster shows that the proposed proactive hybrid autoscaler outperforms the default Horizontal Pod Autoscaler (HPA) of Kubernetes in terms of both QoS and resource utilization efficiency. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

DRESSING UP LLM: EFFICIENT STYLIZED QUESTION-ANSWERING VIA STYLE SUBSPACE EDITING

arXiv

引用

arXiv 2025年

作者： Ma, Xinyu Xu, Yifeng Lin, Yang Wang, Tianlong Chu, Xu Gao, Xin Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China Center on Frontiers of Computing Studies Peking University China National Research and Engineering Center of Software Engineering Peking University China

We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model’s representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

引用

Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

来源：评论

学校读者我要写书评

暂无评论

SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud computing System for Diabetes Prediction Enabled by Ensemble Machine Learning 15

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

引用

15th IEEE International Conference on Cloud computing Technology and Science, CloudCom 2024

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering Al-Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331507589

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：