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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...

引用

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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Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 21

Steel Surface Defect Detection Algorithm Based on Improved Y...

引用

21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Che, Jie Yao, Xinrui Wang, Yuqi Li, Bohan Xu, Hangrui Liu, Jian School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350365221

Surface defect detection technology is a vital component of the steel industry that has garnered significant attention from the academic community in recent times. While modern methods with deep learning-based object detection provide better detection accuracy than traditional approaches, production requirements are often not met by their inference time. This study suggested an improved method based on the industry-leading object detection algorithm YOLOv8 to address this issue and effectively detect steel surface defects while keeping a reasonable balance between speed and accuracy. First, a Stem module based on YOLOv8s is added to downsample the image and lessen the effect of redundant features in the original image. In the meantime, the backbone network gains a large kernel depthwise convolution module called InceptionNeXt+, which improves the model's ability to extract features and has a larger effective receptive field. Lastly, the base model's SPPF module is changed to a RsimSPPF module, which increases the model's accuracy and speeds up inference. The improved YOLOv8 model confirms the efficacy of the improved method with experimental results on the NEU-DET dataset achieving a mAP of 76.9%, increasing by 3.3 compared to the baseline model. © 2024 IEEE.

关键词： Steelmaking

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Enhancement in Context-Aware Recommender Systems - A Systematic Review 2

Enhancement in Context-Aware Recommender Systems - A Systema...

引用

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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Q2Cypher: Converting Natural Language Questions to Cypher with Fine-Tuned Large Language Models 5

Q2Cypher: Converting Natural Language Questions to Cypher wi...

引用

5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Zou, Yunqi Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528911

Knowledge Base Question Answering (KBQA) intends to obtain credible answers to natural language questions based on knowledge bases. Knowledge graphs, also known as graph databases, explicitly present a large amount of factual knowledge and the associations among them. Neo4j, one of the most popular graph databases, uses the Cypher Query Language (CQL) for efficient querying. Due to its complexity, converting natural language questions into CQL, i.e., Text-to-CQL, is in demand, but the performance is undesirable for existing models. To address the issue, we propose Q2Cypher, a Text-to-CQL framework that is able to generate corresponding CQL based on natural language questions by fine-tuning open-source large language models (LLMs). Q2Cypher employs an unsupervised joint retrieval method to further align entity and relation names in the CQL, thus improving generation from the models more directly. We conduct experiments on open-source LLMs such as ChatGLM2, Baichuan2 and Llama-2, using different fine-tuning methods such as LoRA, Freeze and QLoRA for comparison. The experimental results demonstrate that Q2Cypher is effective and achieves better performance than existing methods on the SpCQL dataset. © 2024 IEEE.

关键词： Knowledge graph

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

引用

2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Multi-class Skin Lesion Classification Using Intelligent Techniques 3rd

Multi-class Skin Lesion Classification Using Intelligent Tec...

引用

3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Ranjan, Vibhav Chaurasia, Kuldeep Singh, Jagendra School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819719600

This study explores the development and assessment of an advanced skin cancer classification system utilizing deep learning techniques for accurate discrimination among diverse skin cancer types. The primary aim is to construct an effective classification framework based on deep learning that has the capability of distinguishing melanoma, vascular lesions, melanocytic nevus, cutaneous fibromas, benign keratosis, and various carcinomas and skin moles. Evaluation of the system involves comprehensive analysis employing metrics like accuracy, recall, precision, and F1-score using HAM10000 dataset. We utilized pre-trained models, like EfficientNetB0, VGG19, MobileNet, and ResNet50V2 and analyzed the performance for unbalanced and manually balanced dataset for different hyperparameters. The AI-based automatic system serves as a crucial tool in aiding accurate skin cancer diagnosis. It was found that MobileNet and ResNet50V2 performed quite well as compared to other models after dataset augmentation achieving overall accuracy of 84% and average F1-score across classes of 84% for both MobileNet and ResNet50V2. While performance may vary based on image quality, dataset diversity, and population-specific factors, this research highlights the efficacy and potential of deep learning in advancing skin cancer diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Multi-Modal Knowledge Graph Completion Enhanced by Muti-Level Attention Mechanisms 3

Multi-Modal Knowledge Graph Completion Enhanced by Muti-Leve...

引用

3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Zhang, Tingting Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506582

Multimodel knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Differing from prior Single-model knowledge graph completion efforts, MKGC leverages rich textual descriptions, images, and other multi-model information associated with entities. Traditional MKGC methods often use simple concatennation, weighted fusion, or tensor-based approaches for multi-modal information fusion, but they fail to capture the in-tra-modality and inter-modality dynamics. Moreover, traditional models like TransE and TransH, based on translational operations, struggle with complex relationships such as 1-N, N-N. To address these issue, this paper proposes a multi-modal information fusion enhanced by multi-level attention mechanism and relation-specific graph attention network method for MKGC. Firstly, obtaining text, attribute, and image embeddings of entities by leveraging pre-trained modals such as BERT and Vision Transformer. Secondly, using low-rank tensors for multimodal fusion and introducing self-attention and cross-attention blocks to capture complementary information within and across modalities respectively. Finally, constructing a multi-source node graph and a relation graph attention network is employed to capture the structural information of the graph. The ConvE modal is used as a decoder, and the performance is evaluated through link prediction tasks on real datasets. Experimental results show that new method out-performs other baselines, such as TransE, R-GCN, RSME, KG-BERT. © 2024 IEEE.

关键词： Knowledge graph

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Sparse angle CT reconstruction based on neural radial field 3

Sparse angle CT reconstruction based on neural radial field

引用

3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhao, Dongjie Nan, Hai Zhao, Kai The Chongqing University of Technology School of Computer Science and Engineering Chongqing400054 China

ISBN: (纸本)9781510681279

To address the issues of extensive artifacts and noise in sparse-angle computed tomography (CT) reconstruction, we propose a neural radial field-based sparse-angle CT reconstruction algorithm. Specifically, we utilize both newly synthesized projection views and original projection views as inputs to increase the projection views, thereby enhancing the quality of the reconstruction results. However, the newly synthesized projection images exhibit a certain degree of blur. To address this issue, we incorporate a deblurring module into the network to optimize sampling during the process, thereby achieving deblurring effects. This approach overcomes the challenges of sparse-angle CT reconstruction. We demonstrate CT reconstruction with fewer projection data (20 projections) and validate the effectiveness of our algorithm on a publicly available clinical dataset. Experimental results show that the proposed algorithm can improve the quality of CT image reconstruction, providing a feasible and effective method. © 2024 SPIE.

关键词： Image reconstruction

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Double-Click: dual-click iteration and dual-branch networks for interactive segmentation 16

Double-Click: dual-click iteration and dual-branch networks ...

引用

2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Jiang, Mingcheng Wang, Di Ren, Benzhe School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The main aim of Interactive image segmentation is to accurately segment regions of interest by utilizing simple user-provided interactive information. Currently, the most popular training approach involves using the previous segmentation result as supplementary information for the next segmentation process to establish a connection between the two interactions. This iterative training method transfers all the information carried by interactions to the global level, without considering its impact at the local level. This will affect the final segmentation results and the training efficiency. To address this problem, we propose an iterative training strategy called Double-Click to involve cropping the image based on the newly added clicks and then separately feeding the cropped image and the newly added clicks into a smaller branch network to obtain predictions for the local region. Subsequently, the locally segmented result is fused with the previous segmentation result to achieve the final outcome. Then, the refined segmentation results are used to guide the next segmentation, ensuring that more precise results guide subsequent segmentations while maintaining the guidance information of the clicks on the global image. Our training strategy improves the training effectiveness by handling two clicks in an iterative process. The accuracy of the segmentation is ensured through both local and global predictions. Additionally, we designed a dual-branch network to accommodate our proposed training strategy. The effectiveness of this strategy has been tested on four validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results demonstrate that the Double-Click method achieves better performance with fewer interactions compared to previous methods. © 2025 SPIE.

关键词： Semantic Segmentation

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Large Language Model (LLM) Text Generation Detection: A BERT-BiLSTM Approach with Attention Mechanisms 4

Large Language Model (LLM) Text Generation Detection: A BERT...

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4th International Conference on Electronic Information engineering and computer technology, EIECT 2024

作者： Yan, Jin School of Computer Science and Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9798331528850

With the enhancement of Large Language Models (LLMs) performance, the demand for reliable methods to distinguish AI-generated text from human-written content is steadily growing. This study presents a novel detection framework based on a hybrid architecture integrating BERT, BiLSTM, and Talking-Heads Attention mechanisms. By leveraging BERT's contextual embedding capabilities, BiLSTM's sequential dependency capture, and an enhanced attention layer, the proposed model achieves significant improvements in classification accuracy and robustness. Experiments conducted on the GPT Reddit Dataset (GRiD) demonstrate the efficacy of the model, achieving an accuracy of 98.5% and an F1-score of 96.3%, outperforming many models such as SVM, ALBERT, and RoBERTa. The findings indicate that this approach successfully addresses the challenges posed by increasingly human-like text generated by advanced LLMs, offering a promising solution for maintaining digital content integrity. © 2024 IEEE.

关键词： Natural language processing systems

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