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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Abinaya, S. Ramya, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350328202

A Recommendation System is a crucial Machine Learning algorithm that provides suggestions for various applications and uses computer algorithms or software to analyse and predict user preferences between products, content, services, or information. Within the realm, Recommendation Systems have become vital tools, utilizing Machine Learning algorithms to provide valuable suggestions across various applications. However, traditional Recommendation System encounters challenges related to scalability, sparsity, cold start issues, lack of diversity, and context-based recommendation, which results in user dissatisfaction and limited engagement. Addressing these issues, this study embarks on a comprehensive survey to propose a Context-Aware Recommendation System that integrates contextual factors such as the user's geographical position, current timing, emotional state, company, social engagements, psychological well-being, atmospheric conditions, direction, life stage, sensory information, gender, and personal profile. The survey meticulously investigates the potential enhancement of the Context-Aware Recommendation System by applying Deep Learning methodologies. The survey critically assesses and explores how advanced Deep Learning techniques can synergize with Context-Aware Recommendation System technology, overcome the limitations of current contextual systems, and ultimately optimize the recommendation process for an enriched user experience and enhanced system efficiency. © 2024 IEEE.

关键词： Recommender systems

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Text Sentiment Classification Algorithm Based on BiLSTM Deep Learning 5

Text Sentiment Classification Algorithm Based on BiLSTM Deep...

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5th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2024

作者： Gong, Weiwei School of Computer and Information Engineering Nanchang Institute of Technology Nanchang330044 China

ISBN: (纸本)9798331539818

In recent times, the swift evolution of mobile internet technology and the ubiquitous presence of diverse applications have made e-commerce and social media platforms integral to everyday life. These platforms offer a wide variety of products and services, facilitating user interactions through comment sections where consumers share their experiences. Typically, these comments are textual and rich in emotional expressions and personal perspectives. Accurate analysis of these emotional tendencies is crucial for platforms seeking to comprehend user requirements, refine service quality, elevate satisfaction levels, and execute targeted marketing strategies. To address this need, our study introduces a sentiment classification algorithm leveraging bidirectional long short-term memory (BiLSTM) and deep learning (DL) techniques. This algorithm captures the bidirectional context of text sequences using BiLSTM, enhancing the precision in identifying emotional cues. Experimental outcomes demonstrate that our proposed method outperforms traditional sentiment classification approaches, exhibiting notable gains in both accuracy and recall across various datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Research on Public Opinion Sentiment Analysis Methods for Social Networks 9

Research on Public Opinion Sentiment Analysis Methods for So...

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Xiao, Lizhong Yang, Xinglan Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

In the task of public opinion sentiment analysis, short-text sentiment analysis and public opinion network sentiment evolution models play a crucial role. This paper provides an in-depth discussion of public opinion sentiment analysis, which is divided into two categories: short-text sentiment analysis and public opinion network sentiment *** based on sentiment analysis are further discussed from aspects such as public opinion texts and the impact of public opinion networks, followed by an introduction to datasets construction and model evaluation. Finally, an outlook on future research directions is provided. © 2024 IEEE.

关键词： Network Public Opinion Sentiment Propagation Short-Text Sentiment Analysis

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A Multi-Modal Rumor Detection Method Based on Topic Features and Multi-Level Fusion 24

A Multi-Modal Rumor Detection Method Based on Topic Features...

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2024 7th International Conference on computer Information Science and Artificial Intelligence, CISAI 2024

作者： Yang, Xiaolu Ge, Jike Wang, Zining Yu, Wencheng Zhang, Yifan Xiang, Hao School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798400707254

Rumors can have a negative impact on social life, and multimodal rumors, which combine text and images, are more misleading and spread more widely than text-only rumors. Therefore, detecting multimodal rumors is particularly important. Existing multimodal rumor detection methods primarily focus on contextual information in the text and image information, neglecting other feature information, and simply concatenate features during fusion, which fails to fully utilize the multimodal information. To address these issues, we propose a multimodal rumor detection method based on topic features and multi-level fusion. First, contrastive learning is used to optimize the sentence embeddings of the pre-trained BERT model, enabling the model to learn high-quality embeddings that capture the semantics of sentences. Then, the optimized BERT model is employed to extract contextual semantic features from the text, while the LDA topic model is used to extract topic semantic features from the text. At the same time, the pre-trained ViT model is used to extract features from images, overcoming the limitation of small training samples. Next, a multi-level fusion of various features is performed to fully capture the potential relationships between different features. Finally, the obtained multimodal fused features are fed into the rumor detection module for classification. Experimental results show that our model outperforms mainstream multimodal baseline models on both Twitter and Weibo datasets, enhancing the effectiveness of multimodal rumor detection. © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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An Automated Recommendation Model for E-Commerce Web Application Using Machine Learning 2

An Automated Recommendation Model for E-Commerce Web Applica...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Sheik Abdullah, A. Suseendhar, B. John, Sharon Jefrin, Tanisha School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350366570

The rapid evolution of the Internet and technological advancements have revolutionized the retail landscape, giving rise to e-commerce as a dominant force in the industry. In response to the increasing demand for online shopping, businesses are compelled to develop innovative, user-friendly web applications to cater to evolving consumer needs. This abstract introduces an innovative e-commerce web application designed to transform the online shopping experience. Leveraging cutting-edge technologies, intuitive user interfaces, and personalized recommendations, our web application aims to enhance convenience, engagement, and customer satisfaction. Through a comparative analysis, we demonstrate that our solution surpasses existing methods. Our e-commerce web application boasts intuitive search and filtering capabilities, empowering users to easily navigate through a vast product catalogue. Moreover, sophisticated recommendation algorithms, driven by machine learning techniques, provide personalized product suggestions based on users' browsing and purchasing history, significantly enhancing the overall shopping experience. The web application ensures seamless performance, even during peak traffic periods and also offers integrated secure payment gateways for customers confidence and peace of mind while completing transactions. By combining advanced technology with user-centric design, this e-commerce web application sets a new standard for online shopping, promising increased convenience, engagement and satisfaction for users. © 2024 IEEE.

关键词： Marketplaces

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Advanced Techniques for Chinese Image Captioning: Investigating Attention Mechanisms Based on Object Detection for Chinese Image Caption Generation 5

Advanced Techniques for Chinese Image Captioning: Investigat...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Hua, Yongbin Li, Pei'ang Zeng, Xiangjin Xu, Hong School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan China

ISBN: (纸本)9798331530334

The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds significant theoretical and practical value. Inspired by top-down attention mechanisms, this paper proposes an innovative attention model. Utilizing the output of pretrained object detection networks as prior knowledge for images, the model guides the generation of natural language descriptions. By directly incorporating the results of object detection as attention inputs into the text generation network, the model effectively focuses on key descriptive regions of images, thereby significantly enhancing performance. On public Chinese image captioning datasets, this model demonstrates substantial advantages in metrics such as BLEU-4 and METEOR. © 2024 IEEE.

关键词： Object recognition

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CSA4Rec: Collaborative Signals Augmentation Model Based on GCN for Recommendation 25th

CSA4Rec: Collaborative Signals Augmentation Model Based on ...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Liu, Haibo Yu, Lianjie Si, Yali Liu, Jinglian School of Cyber Security and Computer Hebei University Baoding Baoding China School of Computer Science and Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9789819605699

Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. The augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN model on raw data, before making the final prediction. Despite their success, three problems have always existed. First, during the pre-training phase, over-smooth and data sparsity have a serious impact on the quality of augmented graph data. Second, the influence degree of higher-order collaborative signals are difficult to control, resulting in unstable embeddings. Last, the output of the pre-trained model is only used to augment the data and is not utilized in subsequent training phases, leading to performance penalty. In this study, we propose an efficient Collaborative Signals Augmentation model based on GCN for Recommendation (CSA4Rec). In the pre-training phase, graph contrastive learning is utilized to generate distinct embeddings to address over-smooth and data sparsity. To manipulate the higher-order collaborative signals effectively, the signals are generated from raw data in the modeling phase and aggregated independently by GCN in the training phase. The Residual-like connection is employed to leverage the output of the pre-trained model to improve the convergence speed in the next training phase. Extensive experiments demonstrate the effectiveness and rationality of CSA4Rec. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Towards recognition of open-set speech forgery algorithms by using prototype learning 3

Towards recognition of open-set speech forgery algorithms by...

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3rd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhao, Zuxing Zhang, Haiyan Min, Hai Chen, Yanxiang School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781510680098

Recent advances in machine learning have made forged video and audio more convincing. This poses a threat to the security of individuals, societies and nations. To address this threat, the ASVspoof initiative was conceived to spearhead research on Automatic Speaker Verification (ASV) for anti-spoofing. Currently, most research on ASVspoof has focused on detecting whether speech has been tampered with. However, little attention has been paid to the recognition of speech forgery algorithms. Moreover, in the real world, new forgery algorithms keep emerging, making it difficult to adapt forgery algorithm recognition models trained under closed-set conditions to realistic open-set scenarios. Therefore, we propose a method based on prototype learning and adaptive thresholding for recognizing speech forgery algorithms in open-set. The method uses manifold mixup and dummy prototypes to simulate and recognize unknown speech forgery algorithms. Prototype classification improves the ability to recognize speech forgery algorithms with high similarity. At the same time, it has the advantage of preventing catastrophic forgetting and facilitates subsequent incremental training using samples of newly recognized forgery algorithms. Thus, our method increases the number of recognized categories for forgery algorithms. Experimental results show that our method is effective. The codes are available at https://***/multimedia-security/open-set-recognization. © 2024 SPIE.

关键词： Speech recognition

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A Few-Shot and Anti-Forgetting Network Intrusion Detection System based on Online Meta Learning

A Few-Shot and Anti-Forgetting Network Intrusion Detection S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhen Lu, Yifei Wu, Wenxin Lu, Yun Wang, Hongxiang Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351255

In the actual Internet of Things (IoT) environment, the proportion of abnormal behavior is much lower than that of normal behavior, and abnormal samples are often scarce, so it is a significant challenge to train efficient network intrusion detection systems using limited labeled samples. Meanwhile, intrusion detection systems based on online learning are prone to catastrophic forgetting, which greatly reduces the performance of online models. Previous research has not comprehensively addressed these two issues. Therefore, this paper proposes a few-shot and anti-forgetting network intrusion detection system based on online meta-learning. The system uses meta-learning as the basic algorithm to efficiently utilize data to train well-performing models with very few samples, thereby addressing the problem of insufficient samples;meanwhile, by saving previous models and invoking them when necessary to combat catastrophic forgetting. The experiments on the CIC-IDS2017 and CIC-IDS2018 datasets show that our proposed detection system can maintain good performance over the long term without forgetting. © 2024 IEEE.

关键词： Network intrusion

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