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Transforming Speech Translation: An End-to-End Transformer-LAS-CTC System for Chinese Dialects to Minor Languages

Transforming Speech Translation: An End-to-End Transformer-L...

引用

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Jing Wang Wenhao Fang College of Intelligent Manufacturing City College of Huizhou Huizhou China Department of Computer Science and Technology Hohai University Nanjing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Speech translation involves converting spoken language from one language to text in another. However, traditional speech translation systems use a cascaded structure that has several shortcomings and defects. As global trade and economic development continue to grow, there is an increasing demand for speech translation tasks from Chinese dialects to minor languages. To tackle these issues, this paper proposes an end-to-end transformer- Las-ctcspeech translation system for converting Chinese dialects to minor language text. Traditional systems have limitations, and with globalization, there is an increasing demand for speech translation. The proposed system combines the attention mechanism with a joint LAS-CTC decoder and outperforms traditional models in convergence speed and results. It has the potential to become a useful tool for bridging language barriers and facilitating communication.

关键词： Translation Biological system modeling Computational modeling Globalization Transformers Encoding Decoding Informatics Long short term memory Convergence

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Embedded Operating System Memory-Related Vulnerabilities Detection Based on Taint Analysis and Graph Neural Networks

Embedded Operating System Memory-Related Vulnerabilities Det...

引用

Frontiers Technology of Information and computer (ICFTIC), IEEE international conference on

作者： Bin Wei Xiangji Xi Zishuai Wu Yanyun Lu Chang Guo Zhenfeng Li Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing China Institute of Microelectronics of The Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Institute of Microelectronics of The Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Embedded operating systems are highly customizable, with limited public information and severe security vulnerabilities. Among the vulnerabilities, one-third are memory-related. Traditional detection methods are inefficient and costly for addressing these vulnerabilities. Deep learning-based approaches may cause information loss or introduce irrelevant noise during the code representation process. Moreover, memory-related vulnerability patches are often very similar to the source code, making it difficult to represent vulnerability features. To address these issues, this paper proposes a graph neural network-based detection method using taint analysis. By tracking and slicing the representation graph based on taint information, the method can expand effective graph information and remove irrelevant noise. Experimental results show this method achieves an F1 score of 81.82% on an embedded OS memory-related Vulnerability dataset.

关键词： Analytical models Filters Operating systems Source coding Computational modeling Noise Refining Graph neural networks Security Noise measurement

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A measurement Study of Wechat Mini-Apps 21

A Measurement Study of Wechat Mini-Apps

引用

2021 ACM SIGMETRICS / international conference on measurement and modeling of computer systems, SIGMETRICS 2021

作者： Zhang, Yue Turkistani, Bayan Yang, Allen Yuqing Zuo, Chaoshun Lin, Zhiqiang The Ohio State University Columbus United States

ISBN: (纸本)9781450380720

A new mobile computing paradigm, dubbed mini-app, has been growing rapidly over the past few years since being introduced by WeChat in 2017. In this paradigm, a host app allows its end-users to install and run mini-apps inside itself, enabling the host app to build an ecosystem around (much like Google Play and Apple AppStore), enrich the host's functionalities, and offer mobile users elevated convenience without leaving the host app. It has been reported that there are over millions of mini-apps in WeChat. However, little information is known about these mini-apps at an aggregated level. In this paper, we present MiniCrawler, the first scalable and open-source WeChat mini-app crawler that has indexed over 1,333,308 mini-apps. It leverages a number of reverse engineering techniques to uncover the interfaces and APIs in WeChat for crawling the mini-apps. With the crawled mini-apps, we then measure the resource consumption, API usage, library usage, obfuscation rate, app categorization, and app ratings at an aggregated level in this work. © 2021 Owner/Author.

关键词： Application programming interfaces (API)

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Interpretability Index Based on Balanced Volumes for Transparent Models and Agnostic Explainers

Interpretability Index Based on Balanced Volumes for Transpa...

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IEEE international conference on Fuzzy systems (FUZZ-IEEE)

作者： Daniel Leite Arnab Sharma Caglar Demir Axel-Cyrille Ngomo Department of Computer Science Paderborn University Germany

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

We discuss interpretability and explainability of machine learning models. We introduce a universal interpretability index, JJ, to quantify and monitor the interpretability of a general-purpose model, which can be static or evolve incre-mentally from a data stream. The models can be transparent classifiers, predictors or controllers operating on partitions or granules of the data space, e.g., rule-based models, trees, proba-bilistic clustering models, modular or granular neural networks. Additionally, black boxes can be monitored either after the derivation of a global or local surrogate model as a result of the application of a model-agnostic explainer. The index does not de-pend on the type of algorithm that creates or updates the model, i.e., supervised, unsupervised, semi-supervised, or reinforcement. While loss or error-related indices, validity measures, processing time, and closed-loop criteria have been used to evaluate model effectiveness across different fields, a general interpretability index in consonance with explainable AI does not exist. The index JJ is computed straightforwardly. It reflects the principle of justifiable granularity by taking into account balanced volumes, number of partitions and dependent parameters, and features per partition. The index advocates that a concise model founded on balanced partitions offers a higher level of interpretability. It facilitates comparisons between models, can be used as a term in loss functions or embedded into learning procedures to motivate equilibrium of volumes, and ultimately support human-centered decision making. Computational experiments show the versatility of the index in a biomedical prediction problem from speech data, and in image classification.

关键词： Computational modeling Biological system modeling Decision making Predictive models Data models Loss measurement Time measurement

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Innovative Predictive modeling for Tailored Cirrhosis Management Unveiling Novel Feature

Innovative Predictive Modeling for Tailored Cirrhosis Manage...

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Software, systems and Information Technology (SSITCON), international conference on

作者： V Asis Marceline S Saravanan Department of Computer Science and Engineering SRM University Kattankulathur India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The severe scarring of the liver due to the scar tissue gradually replacing healthy liver tissue results in Cirrhosis. This leads to liver failure, portal hypertension, and liver cancer. Our research uses advanced machine learning models with innovative null handling and feature engineering strategies to enhance predictive accuracy. We apply techniques such as iterative SVM-based imputation and groupwise median imputation, leveraging group-specific statistics to handle missing data, alongside feature selection algorithms to extract informative features. Additionally, we propose the ALBI_SCORE, a novel feature based on clinical insights to boost predictive performance. Stratified 20-fold cross-validation was employed to fine-tune hyperparameters, ensuring robustness and generalizability. Key metrics such as Accuracy, Precision, Recall, and AUROC were used to evaluate the models. Our study found that the GroupWise median imputation technique achieved 90% accuracy, while the SVM-based imputation method achieved 84%. These results demonstrate the effectiveness of our approach in predicting cirrhosis outcomes.

关键词： measurement Accuracy Liver Predictive models Feature extraction Prediction algorithms Imputation Robustness Real-time systems Stakeholders

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Multi-Relational Semantic Awareness for Knowledge Graph Completion

Multi-Relational Semantic Awareness for Knowledge Graph Comp...

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international conference on computers, Communications, and systems (ICCCS)

作者： Jiawei Song Zongtao Duan Jianrong Cao Yun Lin School of Information Engineering Chang'an University Xi'an China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

With the advent of the data era and the wide application of knowledge graph technology, the integrity of knowledge graph has also attracted much attention. The real world is a heterogeneous network graph full of complex relationships. Existing knowledge representation learning to interact with entities and relationships improves the accuracy of knowledge characterization. However, it cannot mine the multi-layered potential semantic information under complex relationships. In order to solve this problem, this study proposes a multi-relationship semantic-aware knowledge graph completion method (MR-SAGCN), which enhances the expressive ability of knowledge triples and the generalization ability of the model by sensing the multi-layer semantic information between entities and the semantic information between relationships under different relationships, and adopting a multi-view decoder approach to enable the model to learn richer representations from different perspectives. Compared with the traditional knowledge representation learning model, our method shows corresponding improvements in various metrics on the generalized datasets FB15k-237 and WN18RR, which proves the effectiveness of our model.

关键词： measurement Accuracy Communication systems Computational modeling Semantics Knowledge graphs Heterogeneous networks

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Response Generation with Personal Attributes and Act Information

Response Generation with Personal Attributes and Act Informa...

引用

international joint conference on Neural Networks (IJCNN)

作者： Haitao Gui Zhongqing Wang College of Computer Science and Technology Soochow University Suzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Despite the remarkable success of personalized response generation models, there is a lack of effective research on how to make dialogue systems more personalized by integrating dialogue act data and personal attributes. In this study, we propose a dialogue acts and personal attributes-based self-supervised model for response generation. To carry out the task, we first add personal attributes and dialogue acts to the sitcom dataset. Then, we employ a self-supervised training technique to give a pre-trained language generation model with knowledge of the unique structural features of dialogue texts, with several mask strategies to merge historical dialogues, personal attributes, and dialogue acts. After that, we construct a personalized template representing personal attributes and dialogue acts. Finally, we take historical dialogues and filled templates as inputs to generate personalized responses with the self-supervised generation model. The experimental results indicate that our response generation model outperforms other strong baselines on several metrics, demonstrating the effectiveness of personal attributes, acts, and our personalized response generation model.

关键词： Training measurement Neural networks Self-supervised learning Data models

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A Framework for Automatic Root Cause Analysis of Network Failure on MPLS Network

A Framework for Automatic Root Cause Analysis of Network Fai...

引用

international joint conference on computer Science and Software Engineering (JCSSE)

作者： Tikumporn Wankvar Apichon Witayangkurn School of Information Computer and Communication Technology. Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

The topology and connectivity of Multiprotocol Label Switching Virtual Private Networks (MPLS VPNs) are highly complex, making it challenging to investigate and verify problems. Investigating and verifying issues within MPLS VPNs poses a significant challenge, especially concerning chain failures, where a single issue can trigger a cascade of failures across the network. Typically, the analysis process is carried out by experienced users who utilize event or alarm messages from network devices or the Network Management System (NMS). However, these messages often necessitate additional information obtained by executing operation and maintenance commands from network devices. This paper aims to develop an Automatic Root Cause Analysis system for MPLS VPNs use- cases, comprising different components: a Message Broker which supports real-time streaming and multiple sources of message types, a Log Template Generating service for creating generic log templates, an Event Identification Service for classifying types of event messages, a Nodes Chain Lookup Service for finding related affected network devices, a Node Test Service for executing commands to verify various network metrics, a Case-based Fault Identification Service serving as the database of expert knowledge or historical fault cases, a Notification Service for notifying through various modern channels, and a Dashboard providing information to network operators about the root cause of network failure and how to handle it. For evaluation, we utilized simulated generated Critical and Major event messages. This evaluation demonstrates the efficiency of the proposed system in terms of automation and speed.

关键词： Multiprotocol label switching Fault diagnosis measurement Root cause analysis Network topology Real-time systems Virtual private networks

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Scaling Approach Over Learning Layer of Deep Learning Model to Reduce the FALSE Error in Binary Classification

Scaling Approach Over Learning Layer of Deep Learning Model ...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S. Gokila D. Helen Asmarech Muluneh Alemu N.V. Suresh Department of Computer Applications Hindustan Institute of Technology and Science Chennai India Department of Computer Applications SRM Institute of Science and Technology Kattankulathur Chennai ASET College of Science and Technology Chennai

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Objective: Worldwide Breast cancer is a main health issue for the women, every year thousands of deaths occurring due to late detection and diagnosis. computer-aided diagnostic detection systems are becoming a useful tool for early cancer cell identification. The proposed work uses the Deep Learning, Convolutional Neural Network (CNN), model to identify the cancerous cells in medical imaging accurately. Method: The proposed work balances the Learning Layers (LL) of CNN model to find the finest binary classification to detect whether the given image is Malignant or Benign. Experiment is carried out on gradual scaling of LL from 1 to 5 with learning cycles 10,15 and 20 respectively for each stage of scale to reduce the False Positive (FP), False Negative(FN) errors in each layer. Result: The performance of the scaled LL is evaluated using the metric like residual error loss, training accuracy in training stage. The model is evaluated with precision, recall, accuracy and $\mathbf{f 1}$-score in the testing stage. By leveraging the power of deep learning, the system achieved an impressive accuracy rate of 97% in differentiating between the two common types of cancer cells in CNN-LL5.

关键词： Deep learning Training measurement Solid modeling Accuracy Computational modeling Data models Convolutional neural networks Biological cells Testing

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Video Action Recognition in Noisy Environments Using Deep Learning Techniques

Video Action Recognition in Noisy Environments Using Deep Le...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Tharuni Dayara Snehitha Choutpally Rishika Gundannagari Dosadi Gagan Reddy Mahesh Kumar Challa Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Accurately identifying actions in noisy environments is challenging due to video degradation and interference. Factors such as visual noise, background clutter, motion artifacts, and low-light conditions can degrade video quality, impacting the accuracy of traditional recognition systems. This project aims to tackle these challenges using advanced techniques and technologies. We leverage the Mediapipe framework, known for its robustness in multimedia processing, and integrate the high-performing video classification model Mo ViNet. Our focus is on integrating advanced noise reduction and robust action recognition methods, including spatial and temporal filtering, adaptive algorithms, deep learning, and feature extraction. By combining these techniques, we propose a comprehensive solution to enhancevideo action recognition in noisy environments, achieving higher accuracy of around 94 percent and reliability across real-world scenarios. This work contributes to advancing video action recognition systems, benefiting applications in surveillance, healthcare, interactive systems,and beyond.

关键词： Deep learning Training Visualization Accuracy Computational modeling Transfer learning Noise reduction Feature extraction Noise measurement Video recording

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