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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Masuma, Fozilatunnesa Chakraborty, Partha Islam, Al-Amin-Ul Talukder, Prince Chandra Roy, Proshanta Yousuf, Mohammad Abu Department of Computer Science and Engineering Comilla University Cumilla3506 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh

ISBN: (纸本)9789819708918

In Natural Language Processing (NLP), a language model is a probabilistic statistical model that estimates the likelihood that a particular sequence of words will appear in a sentence based on the words that came before it. In our experiment, text prompts for evaluating bias were generated using the BERT and GPT-2 language models. Sentiment, toxicity, and gender polarity are the bias measures that we have added in order to measure biases from numerous perspectives. During fine-tuning BERT model, we have achieved 91.48% accuracy on multilabel toxic comment classification. Later, this fine-tuned pretrained model is used for generating text using BOLD dataset prompts. Our work shows a greater percentage of the texts produced by GPT-2 than those produced by BERT which were labeled as toxic. Similar to how it did in the religious ideology sector, BERT's communism prompt resulted in a toxic text. Compared to BERT, GPT-2 produced writings that were more polarized in terms of sentiment, toxicity, and regard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Toxicity

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Empowering privacy and resilience: a decentralized federated learning approach to cyberbullying detection

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Neural Computing and Applications 2024年 1-16页

作者： Khan, Umair Khan, Salabat Mussiraliyeva, Shynar Samee, Nagwan Abdel Alabdulhafith, Maali Shah, Khalid Department of Computer Science COMSATS University Islamabad Attock Campus Islamabad43600 Pakistan Department of Computer Engineering Jeju National University Jeju Special Self-Governing Province Jeju-si63243 Korea Republic of Al-Farabi Kazakh National University Almaty Kazakhstan Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Institute of Computer Science & amp IT University of Science and Technology Bannu Bannu Pakistan

In a rapidly changing digital world, the rise of cyberbullying has become a pressing issue that calls for creative and flexible solutions to detect and prevent it. To address this urgent need, we introduce a novel method: decentralized federated learning for cyberbullying detection using a ring topology network. Traditional federated learning (FL) paradigms have traditionally relied on centralized servers to manage important operations. However, this centralized model faces complex challenges related to privacy vulnerabilities, fairness disparities, and scalability constraints. Our solution brings about a significant change, embracing decentralization as the foundation of a strong and privacy-focused cyberbullying detection framework. This research represents a significant shift away from centralization, as it relies on distributed clients in a ring topology network to collectively carry out crucial FL tasks. Through the redistribution of these responsibilities and the establishment of direct communication channels between neighboring nodes, our approach successfully avoids the challenges related to central server instability and bias. The ring topology architecture creates a decentralized ecosystem, carefully crafted to prioritize the confidentiality of user data while enhancing the resilience and efficiency of the FL process. Our model architecture for cyberbullying detection is carefully crafted, utilizing the powerful capabilities of GRU, LSTM, BERT, and Word2Vec embedding, along with emotional features. This architectural innovation perfectly aligns with the ring topology FL approach, enabling localized updates, efficient aggregation, and flexible adaptability. It is worth mentioning that the BERT model consistently outperforms its competitors, delivering exceptional results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Federated learning

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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. Finally, ML-FFD integrates the multilevel features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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Distributed Event-Triggered Voltage Secondary Control Strategy for Microgrids Considering Load Forecasting

Distributed Event-Triggered Voltage Secondary Control Strate...

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2024 China Automation Congress, CAC 2024

作者： Qin, Chao Zhang, Jiancheng Ma, Lingyu Pang, Shanchen School of Computer Science and Technology China University of Petroleum Qingdao China School of Information Science and Engineering Qilu Normal University Jinan China School of Information Science and Engineering Shandong Management University Jinan China

ISBN: (纸本)9798350368604

Voltage regulation in microgrids (MGs) is increasingly challenging due to the integration of renewable energy sources and load variability. This paper presents a distributed event-triggered voltage secondary control strategy for MGs, incorporating load forecasting using semi-Markov theory. By predicting load variations, the proposed method utilizes an event-triggered communication mechanism to adjust the voltage of distributed generation units, effectively solving voltage regulation issues caused by line impedance mismatches and load fluctuations. Lyapunov stability theory is employed to prove the stability of the event-triggered system with semi-Markov switching topology. Simulation results demonstrate that the proposed control strategy significantly reduces communication frequency while maintaining voltage at its rated value, even under varying load conditions. This approach enhances the reliability and efficiency of MG operations, demonstrating its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Microgrids

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Research on multimodal relation extraction method based on contrast learning 3

Research on multimodal relation extraction method based on c...

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3rd International Conference on Electronic information engineering, Big Data, and computer technology, EIBDCT 2024

作者： Zhao, Jingsheng Ni, Qihui Yan, Shuai Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong266000 China School of Computer Science and Technology Suzhou University Suzhou Jiangsu215006 China

ISBN: (纸本)9781510680449

Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of heterogeneity between multi-modal data, a multi-modal relation extraction model MRECL based on contrastive learning is constructed. Firstly, the feature representation of image and text is obtained through ViT and BERT models;secondly, contrastive learning is introduced to align image-text features to reduce modality heterogeneity in advance;then image is used as prefix guidance information, and multi-modal interaction fusion is carried out with text information through attention mechanism;finally, the probability of all relation classification is calculated through Softmax layer and the prediction relation is output. Experimental results show that the precision, recall, and F1 values of MRECL on the MNRE dataset reach 83.23%, 82.19%, and 82.70%, respectively, achieving better performance than the current SOTA model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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A QoS-Aware Traffic Management Policy for IoT-enabled Smart City Applications based on Edge Cloud Computing 6

A QoS-Aware Traffic Management Policy for IoT-enabled Smart ...

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6th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2022

作者： Absardi, Zeinab Nazemi Javidan, Reza Shiraz University of Technology Department of Computer Engineering and Information Technology Shiraz Iran

ISBN: (数字)9781665488914

ISBN: (纸本)9781665488914

The advances of IoT have facilitated the development of many areas such as smart cities, smart homes, smart health care, etc. However, managing the big amount of data generated in these domains requires the right traffic policies. Though, edge computing along with cloud computing is a viable solution for IoT data processing, Quality of Service (QoS) factors should be considered when sending data between the edge and the cloud over the infrastructure network. In this paper, a QoS aware traffic management policy in IoT based on edge cloud computing is proposed. A billing system of a smart city in which, some smart meters generate data continuously and send it to the edge and cloud data centers for further processing, is simulated as an evaluation scenario. The experimental results showed that the proposed policy could impressively reduce the application total running time and the energy consumption of the smart meters. © 2022 IEEE.

关键词： Quality of service

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Probe Machine Based Computing Model for Maximum Clique Problem

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Chinese Journal of Electronics 2022年第2期31卷 304-312页

作者： CUI Jianzhong YIN Zhixiang TANG Zhen YANG Jing Department of Computer Huainan Union University School of Electronic and Information Engineering Anhui University of Science & Technology School of Mathematics Physics and Statistics Shanghai University of Engineering Science School of Mathematics and Big Data Anhui University of Science & Technology

Probe machine(PM) is a recently reported mathematic model with massive parallelism. Herein,we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently,we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution *** the other hand, the proposed model is highly *** work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.

关键词： maximal cliques maximum clique problem searching capacity data library NP-complete problem computational complexity model lie induced subgraph probe operation undirected graph mathematic model graph theory parallel algorithms optimisation parallelism computing model vertex probe library probe machine

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MediClass-RF: An Optimized Random Forest Framework for Medicinal Plant Classification with Feature Importance and Comprehensive Performance Evaluation

MediClass-RF: An Optimized Random Forest Framework for Medic...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Sapna, R. Sheshappa, S.N. Raja, S Pravinth Preethi Devadas, Raghavendra M Hiremani, Vani Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India Sir Mvit Department of Information Science and Engineering Bangalore India Presidency University Department of Computer Science and Engineering Bangalore India University Pune India

ISBN: (纸本)9798331533311

Proper categorization of medicinal and non-medicinal herbs is essential for further application in healthcare and the herbal industries. In our work, we proposed MediClass-RF, the Random Forest-based optimized framework aimed at medicinal plant classification, providing feature importance analysis and in-depth performance metrics evaluation. MediClass-RF was trained on a dataset that contains instances of real-world medicinal plants and non-medicinal plants. The popular term Frequency-Inverse Document Frequency approach fed these plants' properties into the modules, and synthetic ones with labels of different quality were added artificially to control noise levels and, therefore, simulate practical usage more accurately. These plants' properties were fed into the model using a popular approach. The Random Forest model was trained, tested, and validated with the normal 70:30 separation of training and testing sets, and it performed well, scoring 89% overall accuracy. In terminologies of performance, the model had a recall of 0.71, indicating its capability to capture a significant portion of actual medicinal plants, and an F 1-score of 0.83, signifying stability among precision with recall. According to the feature importance study, the most important features leading to classification are terms with anti-inflammatory, antioxidant, and antibacterial activities. Furthermore, an Area Under the Curve (AUC) suggests the model's great power in recognizing medicinal properties against non-medicinal plants. Cross-validation was done five times to validate the model further, obtaining a mean accuracy score of 0.88, calculated out of 0.875,0.91666667, 0.875,0.83333333,0.91666667, demonstrating the model's robustness across diverse subsets of the data. This work shows the possibility of machine learning, specifically Random Forest, in medicinal plant classification, making it a valuable tool for medicinal plant specialists and investigators. © 2024 IEEE.

关键词： Medicinal chemistry

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Leveraging AI for Enhanced Botnet Detection - A Review of Machine Learning Approach for Cybersecurity 3

Leveraging AI for Enhanced Botnet Detection - A Review of Ma...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sonune, Harsha Kulkarni, Nilima Mksss's Cummins College of Engineering for Women Department of Information Technology Pune India Mitadt University Mitsoe Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350354348

Botnets represent a substantial threat to cybersecurity, enabling malicious actors to execute large-scale attacks that compromise network integrity and data confidentiality. Current detection methods often fall short in addressing the rapidly evolving strategies employed by botnets. This study has an aim of designing and developing a sophisticated botnet detection system leveraging artificial intelligence (AI) techniques. By integrating machine learning algorithms and advanced data analysis methods, the proposed system seeks to accurately identify and mitigate botnet activities in real-time. The study will evaluate the effectiveness of various AI models in detecting botnet traffic, provide a comparative analysis of their performance, and propose an optimal framework for implementation in cybersecurity infrastructures. © 2024 IEEE.

关键词： Bot (Internet)

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HY-PC:Enabling Consistent Positioning and Communication Using Visible Light

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China Communications 2023年第4期20卷 180-194页

作者： Xiangyu Liu Yaoheng Chen Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Visible light(VL)plays an important role in achieving high-precision positioning and low bit error radio(BER)data ***,most VL-based systems can not achieve positioning and communication,*** are two problems:1)the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance,2)in the hybrid systems,the lost data bits in the inter-frame gap(IFG)are hard to recover,which affects positioning and communication ***,in this article,we propose a novel VL-based hybrid positioning and communication system,named HY-PC system,to solve the above ***,we propose the robust T-W mapping for recognizing specific Light Emitting Diodes(LEDs),which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication ***,we also propose the novel linear block coding and bit interleaving mechanism,which can recover the lost data bits in the IFG and improve data communication ***,we use commercial off-the-shelf devices to implement our HY-PC system,extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication,simultaneously.

关键词： visible light positioning visible light communication T-W Mapping linear block coding bit interleaving

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