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22nd International Conference on ICT and Knowledge Engineering, ICT and KE 2024

作者： Nwaiwu, Steve Jongsawat, Nipat Tungkasthan, Anucha Thaloey, Jarinya RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand RMUTT Big Data Analytics and Management Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350391411

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models. © 2024 IEEE.

关键词： Natural language processing systems

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Cubic Semisymmetric Graphs of Order 2qp^2

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Acta Mathematicae Applicatae Sinica 2019年第3期35卷 629-637页

作者： Xiao-hui HUA Song-tao GUO Li CHEN Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471003China

A regular edge-transitive graph is said to be semisymmetric if it is not *** p be a *** Folkman[*** 3(1967),215–232],there is no cubic semisymmetric graph of order 2p or 2p^2,and by Hua et al.[Science in China A 54(2011),1937–1949],there is no cubic semisymmetric graph of order 4p^*** et al.[Science in China A 47(2004),11–17]classified connected cubic semisymmetric graphs of order 6p^*** this paper,for p>q≥5 two distinct odd primes,it is shown that the sufficient and necessary conditions which a connected cubic edge transitive bipartite graph of order 2qp^2 is semisymmetric.

关键词： Bi-Cayley graph edge-transitive graph semisymmetric graph regular covering

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Traceability and Identity Protection in Smart Agricultural IoT System Framework Based on Blockchains

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Xie, Mingyue Liu, Jun Chen, Shuyu Lin, Mingwei Xu, Guangxia Xu, Zeshui Chang, Zheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Chongqing University School of Big Data and Software Engineering Chongqing400065 China Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China Sichuan University Business School Chengdu610000 China University of Jyväskylä Faculty of Information Technology P.O. Box 35 JyväskyläFIN-40014 Finland

Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented. © 2004-2012 IEEE.

关键词： Crop protection

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Numerical study of thermal performance of silica-aerogel/paraffin nanostructure in the presence of CuO nanoparticles: A molecular dynamics approach

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Nano-Structures and Nano-Objects 2025年 41卷

作者： Ali, Ali B.M. Hussein, Rasha Abed Babadoust, Shahram Singh, Narinderjit Singh Sawaran Salahshour, Soheil Baghaei, Sh. Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Dentistry Al-Manara College for Medical Sciences Maysan Amarah Iraq Department of Medical Biochemical Analysis Cihan University-Erbil Kurdistan Region Erbil Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Faculty of Science and Letters Piri Reis University Tuzla Istanbul Turkey Department of Engineering Islamic Azad University Iran

The rise in air pollution and fuel costs increased the use of various renewable energy options. Currently, scientists face a significant challenge. Finding methods to store energy that can be easily converted is crucial. There is growing interest in using phase change materials for thermal energy storage systems. This interest stems from their ability to conserve energy and reduce air pollution. Silica aerogel effectively maintains the temperature of items over long periods. Phase change materials, recognized for storing thermal energy, are now favored for preserving both hot and cold temperatures. This study aimed to use computer simulations to understand the behavior of silica aerogel/PCM and CuO nanoparticles in a cube. The results show that the nanostructure can achieve a velocity of 0.0086 Å/fs and had a thermal conductivity of 1.85 W/m·K. These findings may have practical applications in heating and cooling systems, energy storage, and the aerospace industry. © 2025 Elsevier B.V.

关键词： Copper Oxide Molecular Dynamics Simulation Nanocomposite Nanoparticles Phase Change Materials Silica Aerogel

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Design of marketing scenario planning based on business big data analysis 2nd

Design of marketing scenario planning based on business big ...

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2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015

作者： Hong, Seungkyun Shin, Sungho Kim, Young-min Seon, Choong-Nyoung Um, Jung ho Song, Sa-kwang Korea Institute of Science and Technology Information Daejeon Korea Republic of Department of Big Data Analysis Korea University of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9783319208947

As the amount and the type of data for business decision making are rapidly increasing, the importance of big data analytics is gradually critical for making effective business strategy. However, big data analytics based decision making systems basically requires distributed parallel computing capability in order to make timely business strategy recommendation via processing huge amount unstructured as well as structured business data. We introduce a big data analytics system for automatic marketing scenario planning based on big data platform software such as Hadoop and HBase. The analytics methodology for scenario planning is based on prescriptive analytics which is the most advance methodology consisting of generation of business scenarios and their optimization, among the three analytics of descriptive, predictive, and prescriptive analytics. Additionally, we developed a prototype of marketing scenario planning system and its graphical user interface, as well as the system architecture based on Hadoop eco-system based distributed parallel computing platform. © Springer International Publishing Switzerland 2015.

关键词： big data

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Design and implementation of commercial SMS supervision model based on blockchain 5

Design and implementation of commercial SMS supervision mode...

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5th International Conference on Mechatronics and Computer technology Engineering, MCTE 2022

作者： Yi, Xianying Lin, Shaofu Jia, Xiaofeng Gao, Song Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Big Data Centre Beijing100101 China

ISBN: (纸本)9781510661073

With the rapid development of the telecommunications industry, commercial short message service (SMS) provides enterprises with low-cost marketing channels. The increasing illegal marketing behavior not only causes troubles for mobile users, but also has a negative impact on the normal supervision of the industry. Under the traditional operation mode, it is challenging to know whether enterprises or service providers (SP) provide commercial SMS according to the wishes of mobile users. Another reason that makes the regulation of commercial SMS more challenging is the lack of transparency in the identification of business entities in the existing commercial SMS service model. Usually, the business SMS service process will generate corresponding communication records, called "Call Details Record" (CDR). When dealing with user complaints or violations, industry regulators spend a long time verifying the effectiveness of CDR, which is also one of the reasons for the decline in the supervision of commercial SMS. In view of the above problems, this paper proposes a commercial SMS supervision model based on blockchain that uses blockchain technology to optimize the traditional SMS supervision methods, and combines smart contracts and asymmetric encryption algorithms to achieve the security and trust of CDR management. The model aims to solve the problems of fuzzy identity of business subjects in commercial SMS, difficulty in CDR traceability of commercial SMS, and long time-consuming CDR verification in the process of SMS supervision. At the same time, the model is conducive to reducing the obstacles of data sharing among multiple parties. Finally, this research designs and implements a business SMS supervision prototype system based on blockchain under Hyperledger Fabric. The performance test results demonstrate the feasibility of the model proposed in this research. © 2022 SPIE.

关键词： Blockchain

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New constructions of q-variable 1-resilient rotation symmetric functions over Fp

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Science China(information Sciences) 2016年第7期59卷 240-242页

作者： Jiao DU Shaojing FU Longjiang QU Chao LI Shanqi PANG College of Science National University of Defense Technology School of Mathematics and Information Science Henan Normal University Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University College of Computer Science National University of Defense Technology

Dear editor,Motivated by Refs.[1–10],we devote to constructing a class of q-variable 1-resilient rotation symmetric functions（RSFs）over the finite field F;={0,1,...,p-1}in this *** this letter,let p and q be differ... 详细信息

关键词： over New constructions of q-variable 1-resilient rotation symmetric functions over F_p

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Quantitative analysis of the content of nitrogen and sulfur in coal based on laserinduced breakdown spectroscopy: effects of variable selection

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Plasma Science and technology 2020年第7期22卷 36-43页

作者： Fan DENG Yu DING Yujuan CHEN Shaonong ZHU Feifan CHEN Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science&TechnologyNanjing 210044People's Republic of China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing 210044People's Republic of China Jiangsu Engineering Research Center on Meteorological Energy Using and Control Nanjing University of Information Science&TechnologyNanjing 210044People's Republic of China

Coal is a crucial fossil energy in today’s society,and the detection of sulfir(S) and nitrogen(N)in coal is essential for the evaluation of coal ***,an efficient method is needed to quantitatively analyze N and S content in coal,to achieve the purpose of clean utilization of *** study applied laser-induced breakdown spectroscopy(LIBS) to test coal quality,and combined two variable selection algorithms,competitive adaptive reweighted sampling(CARS) and the successive projections algorithm(SPA),to establish the corresponding partial least square(PLS) *** results of the experiment were as *** PLS modeled with the full spectrum of 27,620 variables has poor accuracy,the coefficient of determination of the test set(R^2 P) and root mean square error of the test set(RMSEP) of nitrogen were 0.5172 and 0.2263,respectively,and those of sulfur were0.5784 and 0.5811,*** CARS-PLS screened 37 and 25 variables respectively in the detection of N and S elements,but the prediction ability of the model did not improve ***-PLS finally screened 14 and 11 variables respectively through successive projections,and obtained the best prediction effect among the three *** R^2 P and RMSEP of nitrogen were0.9873 and 0.0208,respectively,and those of sulfur were 0.9451 and 0.2082,*** general,the predictive results of the two elements increased by about 90% for RMSEP and 60% for R2 P compared with *** results show that LIBS combined with SPA-PLS has good potential for detecting N and S content in coal,and is a very promising technology for industrial application.

关键词： variable selection LIBS coal CARS and SPA

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Bidal@imagecleflifelog2019: The role of content and context of daily activities in insights from lifelogs 20

Bidal@imagecleflifelog2019: The role of content and context ...

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20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019

作者： Dao, Minh-Son Vo, Anh-Khoa Phan, Trong-Dat Zettsu, Koji Big Data Analytics Laboratory National Institute of Information and Communications Technology Japan Faculty of Information Technology University of Science VNU-HCMC Viet Nam

imageCLEFlifelog2019 introduces two exciting challenges of getting insights from lifelogs. Both challenges aim to have a memory assistant that can accurately bring a memory back to a human when necessary. In this paper, two new methods to tackle these challenges by leveraging the content and context of daily activities are introduced. Two backbone hypotheses are built based on observations: (1) under the same context (e.g., a particular activity), one image should have at least one associative image (e.g., same content, same concepts) taken from different moments. Thus, a given set of images can be rearranged chronologically by ordering their associative images whose orders are known precisely, and (2) a sequence of images taken during a specific period can share the same context and content. Thus, if a set of images can be clustered into sequential atomic clusters, given an image, it is possible to automatically find all images sharing the same content and context by first finding the atomic cluster sharing the same content, then watershed reward and forward to find other clusters sharing the same context. The proposed methods are evaluated on the imageCLEFlifelog 2019 dataset and compared to participants joined this event. The experimental results confirm the high productivity of the proposed method in both stable and accuracy aspects. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Watersheds

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