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Topic-Based Semantic Enhancement for Recommendations

Topic-Based Semantic Enhancement for Recommendations

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computer Science and Blockchain (CCSB), international conference on

作者： Dongjiao Liu Ting Jiang College of Information Engineering Nanjing University of Finance and Economics Nanjing China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

The massive natural language text produced in the information age contains rich information, how to use text information for recommendation is a hot content in the recommendation field. With the development of deep learning, preprocessing models such as BERT have shown very good results in processing text features. However, BERT is difficult to perform structured semantic extraction and performs poorly on structured text data. For this reason, this paper proposes a model T-DGCF based on topic semantic enhancement, which convolves the structured information and content information of the text through the topic model, and takes the obtained text topic vector as the initial embedding of the products. Then, we obtain the user's embedding about the topic through the interaction relationship. The two-part embedding is used as the initial embedding of the recommender system network for recommendation. We conducted experiments on two public datasets of different sizes and compared them with some competing baseline models. The experimental results show that T-DGCF outperforms several advanced recommendation models in two commonly used evaluation metrics.

关键词： Representation learning measurement Computational modeling Semantics Natural languages Information age Feature extraction Vectors Graph neural networks Recommender systems

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Detecting Phising Website using Machine Learning Methods

Detecting Phising Website using Machine Learning Methods

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computer Sciences, Engineering, and Technology Innovation (ICoCSETI), international conference on

作者： Ghulam Alvi Rahmat Riyanarto Sarno Kelly Rossa Sungkono Agus Tri Haryono Abdullah Faqih Septiyanto Sholiq Faculty of Intelligent Electrical and Informatics Technology Institut Sepuluh Nopember Surabaya Jawa Timur Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Modern phishing is a sophisticated cyberattack. It steals login details and payment information by posing as a trusted entity. With our increasing dependence on digital platforms, the frequency and sophistication of phishing attacks have escalated. Many studies have examined phishing detection. Most prioritize metrics like accuracy, precision, and recall. However, a significant research gap exists as previous studies have primarily focused on accuracy and evaluation metrics without incorporating validation processes to verify whether the developed models are adequately robust and reliable. Harnessing the power of machine learning, we can classify websites into authentic or fraudulent categories, proposing a robust defense against these malicious schemes. The purpose of this study is to present the novelty of a concise summary of techniques for detecting phishing and to create a framework that can be used to detect phishing. The dataset comprises 662,590 entries and 9 features. This study implements three supervised learning models: Decision Tree, K-Nearest Neighbors (KNN), and XGBoost algorithms. These algorithms were chosen for their dataset knowledge and applicability. According to experiments, the Decision Tree model has the lowest accuracy at 88.66% and the KNN model the highest at 88.94%. The XGBoost model records an accuracy of 90.24%. XGBoost often achieves high accuracy due to its gradient boosting framework, which combines multiple decision trees to minimize errors. Its regularization techniques also help prevent overfitting, leading to robust performance on unseen data.

关键词： measurement Technological innovation Accuracy Phishing Computational modeling Nearest neighbor methods Real-time systems Decision trees Reliability Load modeling

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MF-BERT: A Siamese Pre-training Framework for Motion Forecasting

MF-BERT: A Siamese Pre-training Framework for Motion Forecas...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianxin Shi Jinhao Chen Xiaolong Chen Jun Ma Tianyu Wo School of Computer Science and Engineering Beihang University Beijing China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately predicting the future motions of traffic agents is essential for autonomous systems. Despite the significant success of existing motion forecasting methods based on supervised learning, they still exhibit two main limitations. First, when annotated data for a scene is limited, these methods often fail to achieve the expected accuracy. Second, they typically rely on complex architectures and extensive prior knowledge to improve performance. To overcome these challenges, we propose MF-BERT, a novel framework that adapts the concept of BERT to motion forecasting, inspired by advancements in the self-supervised pre-training paradigm. During pre-training, we design a siamese sequence modeling task with an asymmetric mask strategy to capture complex behavior patterns of agents. During fine-tuning, the pre-trained representation module initializes the feature encoder of the motion forecasting model, and a multimodal trajectory decoder generates all possible predictions. Experimental results demonstrate the superiority of MF-BERT over state-of-the-art methods.

关键词： measurement Supervised learning Self-supervised learning Predictive models Signal processing Feature extraction Trajectory Forecasting Speech processing Standards

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RoBERTa-based Multimodal Sentiment Analysis of Detecting Teachers’ Emotions

RoBERTa-based Multimodal Sentiment Analysis of Detecting Tea...

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Intelligent Education and Intelligent Research (IEIR), international conference on

作者： Nuo Lei Hao Meng Xingpeng Jiang Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Science Central China Normal University Wuhan China

ISBN: (数字)9798331519827

ISBN: (纸本)9798331519834

Multimodal sentiment analysis, which combines data from various sources such as text, images, and audio, has become a vital tool in educational settings for monitoring teachers’ emotions in the classroom and ensuring teaching quality. We propose a novel model called RoMSA (RoBERTa-based Multimodal Sentiment Analysis) to enhance the effectiveness of this process. RoMSA utilizes a new multimodal joint embedding mechanism that fully captures cross-modal information, and it incorporates a Random Modal Masked Language modeling (RMMLM) task to improve the model’s understanding of different modalities. By leveraging the extensive textual knowledge obtained during pretraining, RoMSA significantly improves the accuracy of multimodal sentiment analysis. Tested on the CMU-MOSI and CMU-MOSEI datasets, RoMSA achieved state-of-the-art results, demonstrating its effectiveness in detecting and analyzing emotions. Through the analysis of teachers’ emotions during classes, multimodal sentiment analysis models can output teachers’ emotional states, helping to ensure teaching quality and enabling educators to optimize their teaching methods to maintain a positive classroom atmosphere.

关键词： Sentiment analysis Analytical models Accuracy Atmospheric modeling Education Transformers Real-time systems Monitoring

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Straight Through Gumbel Softmax Estimator based Bimodal Neural Architecture Search for Audio-Visual Deepfake Detection

Straight Through Gumbel Softmax Estimator based Bimodal Neur...

引用

IEEE international joint conference on Biometrics (IJCB)

作者： Aravinda Reddy PN Raghavendra Ramachandra Krothapalli Sreenivasa Rao Pabitra Mitra Vinod Rathod Indian Institute of Technology Kharagpur Norwegian University of Science and Technology

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Deepfakes are a major security risk for biometric authentication. This technology creates realistic fake videos that can impersonate real people, fooling systems that rely on facial features and voice patterns for identification. Existing multimodal deepfake detectors rely on conventional fusion methods, such as majority rule and ensemble voting, which often struggle to adapt to changing data characteristics and complex patterns. In this paper, we introduce the Straight-through Gumbel-Softmax (STGS) framework, offering a comprehensive approach to search multimodal fusion model architectures. Using a two-level search approach, the framework optimizes the network architecture, parameters, and performance. Initially, crucial features were efficiently identified from backbone networks, whereas within the cell structure, a weighted fusion operation integrated information from various sources. An architecture that maximizes the classification performance is derived by varying parameters such as temperature and sampling time. The experimental results on the FakeAVCeleb and SWAN-DF datasets demonstrated an impressive AUC value 94.4% achieved with minimal model parameters. https://***/Aravinda27/STGS-BMNAS

关键词： Deepfakes Microprocessors Biological system modeling Neural networks computer architecture Detectors Network architecture Biometric authentication Security Facial features

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A Novel CNN-Transformer Framework for COVID-19 Diagnosis from ECG Images

A Novel CNN-Transformer Framework for COVID-19 Diagnosis fro...

引用

computer Information Science and Application Technology (CISAT), international conference on

作者： Xiaofei Huang Qingchao Xu Lisha Pei Yuecheng Cai College of Information Engineering Dalian University Dalian China College of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Research has demonstrated that COVID-19 impacts both the respiratory and cardiovascular systems. Timely and accurate detection of COVID-19 infection using electrocardiogram (ECG) images holds significant research importance. Most researchers have implemented convolutional neural networks (CNNs) to develop COVID-19 diagnostic models based on electrocardiograms (ECGs). However, such models primarily focus on local feature extraction and often lack understanding of inter-lead relationships. This study proposes a novel CNN-transformer framework that enhances the understanding and utilization of inter-lead features by leveraging CNN with CBAM for effective local feature extraction and Transformer encoders for generating rich, stable, and globally representative features. Finally, a classifier based on pre-trained DenseNet161 is employed for diagnosis. The proposed model achieves excellent performance across various evaluation metrics on the COVID-19 dataset, notably achieving an F1 score of 0.98, precision and specificity of 1.0, demonstrating robust diagnostic capabilities.

关键词： COVID-19 measurement Information science Accuracy Computational modeling Electrocardiography Lead Feature extraction Transformers Convolutional neural networks

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Addressing the Class Imbalance Problem in Network-Based Anomaly Detection

Addressing the Class Imbalance Problem in Network-Based Anom...

引用

international conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Toya Acharya Annamalai Annamalai Mohamed F Chouikha Electrical and Computer Engineering Prairie View A&M University Prairie View Texas

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Network anomaly detection systems are vital for identifying malicious activities in computer networks. However, they face a challenge due to class imbalance, where normal traffic outweighs anomalies. This bias leads to models favoring majority classes, neglecting minority anomalies. In this study, we proposed a comprehensive approach to address this issue in network anomaly detection using NSL-KDD and UNSW-NB15 datasets. Our method incorporated techniques like random over-sampling (ROS), random under-sampling (RUS), Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE combined with Edited Nearest Neighbors (SMOTEENN), and class reduction. We evaluated our approach on these datasets, showing improved performance metrics for bidirectional long-short memory (Bi-LSTM). Our results highlight the importance of addressing class imbalance for robust network anomaly detection, contributing to cybersecurity in modern networks.

关键词： Training measurement Adaptation models Computational modeling Data integrity Encoding computer networks

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Research and Application of joint Inspection Technology for Box Type Intelligent Substations

Research and Application of Joint Inspection Technology for ...

引用

Advances in Electrical Engineering and computer Applications( AEECA), 2020 IEEE international conference on

作者： Xinzhuo Li Li Zhang Junjie Zhang Jinxin Li Electric Power Research Institute of Guizhou Power Grid Company Limited Guiyang China Zunyi Power Supply Bureau of Guizhou Power Grid Company Limited Zunyi China

ISBN: (纸本)9798350355260

In order to solve the pain points of long inspection time, low efficiency, significant personal and traffic risks, as well as the lack of scientific and efficient on-site operation safety control measures for box type substations. Starting from the infrastructure construction stage, relying on a large number of intelligent electrical equipment and sensing terminals, key technologies such as multi-source data collection based on intelligent terminals, multi-dimensional big data analysis, data fusion of multiple subsystems, and immersive interaction in multiple scenarios through GIM modeling are utilized. We have researched and developed a multi-dimensional inspection system suitable for box type intelligent substations, which can achieve real-time perception of equipment status, one-step intelligent operation, continuous safety control, and one click information exchange between virtual and real information. The practical application results in a 35kV box type substation in Guizhou show that the system can greatly solve the pain points of box type substation inspection work, completely change the traditional operation and inspection mode of box type substations, and effectively promote the optimization of production organization mode in the field of substation and the construction and development of intelligent substations.

关键词： Analytical models Substations Pain Data integration Inspection Data models Time measurement Safety Maintenance Sensors

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Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and MobileNetV2 for Accurate MRI Diagnostics

Deep Learning Advances in Brain Tumor Classification: Levera...

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international conference on Power, Energy, Control and Transmission systems (ICPECTS)

作者： Alex David S Almas Begum S Ismail Kalilulah Ruth Naveena N Rajathi K Hemalatha D Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Dr. M. G. R. Educational and Research Institute Chennai India Department of Mathematics Hindustan Institute of Technology & Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

These modern deep learning models, such as VGG16 and MobileNetV2 will be utilized in this study to classify Brain tumors from a unique specialized MRI dataset The dataset consists of brain tumor original and augmented images as a two-class (yes/no) classification problem domains, where yes indicates the presence of a tumor while learns no corresponds to its absence. Two models VGG16 and MobileNetV2 support deep convolutional layer operations, well known for their very deep architecture; the other one is optimized over efficiency with depth-wise separable convolutions and is used to evaluate their performance in accurately classifying MRI images. The models use similar data preprocessing and regularization techniques; however, their performance metrics are different. Accuracy, precision, recall, and F1-score are the evaluation metrics for each of the five models to understand how well these differentiate between tumor images versus non-tumor images. This study is designed to improve the ability of brain tumor detection, thereby assisting in more reliable diagnosis and clinical decision-making.

关键词： measurement Deep learning Accuracy Magnetic resonance imaging Decision making Data preprocessing Brain modeling Reliability engineering Control systems Data models

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Posture and Appearance Fusion Network for Driver Distraction Recognition 17th

Posture and Appearance Fusion Network for Driver Distraction...

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17th international conference on Wireless Algorithms, systems, and Applications (WASA)

作者： Yu, Hao Zhao, Chong Wei, Xing Zhai, Yan Chen, Zhen Sun, Guangling Lu, Yang Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Hefei Univ Technol Intelligent Mfg Inst Baohe Peoples R China Hefei Univ Technol Engn Qual Educ Ctr Undergrad Sch Hefei Peoples R China Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Beijing Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei Peoples R China

ISBN: (纸本)9783031192074;9783031192081

Distracted driving is the act of driving while engaged in other activities, such as using a cell phone, texting, eating, or reading, which takes the driver' attention away from the road. Nowadays, the distracted driving detection models based on deep learning can extract critical information from video data to characterize the driving behavior process. But the distraction driving method based solely on appearance features cannot essentially eliminate the noise impact of the complex environment on the model, and the distracted driving recognition method based solely on skeletal information is unable to recognize the joint action of the human body and the objects. Therefore, the development of an accurate distracted driving detection model has become challenging. In this paper, we propose a distracted driving recognition model MFD-former based on the fusion of posture and appearance. First, a feature extraction module is proposed to extract skeleton data(i.e., posture) and appearance features (i.e., descriptors), which are merged by a graph neural network. Then, the two kinds of information are input into the MFD-former encoder module, and the self-attention mechanism quickly extracts the sparse data. Finally, the classification results of distracted driving are obtained by extracting the classification labels through the MLP Head. The MFD-former model outperforms existing models. It achieved 95.1% accuracy on the State Farm dataset and 90.24% accuracy on the self-built Train Drivers dataset.

关键词： Driver distraction recognition Attention mechanism Graph neural network Heterogeneous information fusion

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