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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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A Survey of LLM Datasets:From Autoregressive Model to AI Chatbot

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Journal of Computer Science & Technology 2024年第3期39卷 542-566页

作者：杜非马新建杨婧如柳熠罗超然王学斌姜海鸥景翔 National Key Laboratory of Data Space Technology and System Beijing 100195China Advanced Institute of Big Data Beijing 100195China Fu Foundation School of Engineering and Applied Science Columbia UniversityNY 10027U.S.A. School of Software and Microelectronics Peking UniversityBeijing 100091China CCF IEEE

Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant ***,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically *** datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune *** paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art ***,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and *** study then critically figures out the problems and research trends that exist in current LLM *** study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data *** the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat *** survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.

关键词： large language model(LLM) autoregressive model AI chatbot natural language processing(NLP)corpora OpenAI

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Non-Autoregressive Sign Language Translation: A Transformer Encoder-only Approach for Enhanced Multimodal Integration 24

Non-Autoregressive Sign Language Translation: A Transformer ...

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2024 International Conference on Advanced Robotics, Automation engineering and Machine Learning, ARAEML 2024

作者： Du, Yao Xie, Pan Peng, Taiying Hu, Xiaohui School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400717116

Sign language translation aims to transform continuous sign language videos into text, thereby mapping sign language actions onto natural language. Current research primarily focuses on the Transformer's encoder-decoder architecture, employing an autoregressive (AR) approach for SLT. This one-by-one token generation method has a slow speed to translate. We design a Non-Autoregressive Sign Language Translation (NA-SLT) model based on Transformer Encoder. The proposed model consists of three components: The text-encoder, sign-encoder, and sign-Text-encoder. Both the text-encoder and sign-encoder utilize the Encoder to model single-modality data. The sign-Text-encoder effectively engages with multi-modal data, producing high-quality corresponding text after minimal refinement iterations. Moreover, during model training, we devise a double-mask strategy. In addition to traditional random masking, the schedule allocates mask probabilities based on each word's frequency, ensuring long-Tail tokens have an increased chance of being masked and receiving extensive training. We evaluate the model on PHOENIX-2014T, and the results indicate that NA-SLT approaches the performance of AR models on BLEU (21.06) and ROUGE(49.59) metrics while significantly improving translation speed (5.4x-7.9x). © 2024 ACM.

关键词： Metadata

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Optimizing Hate Speech Detection in the Indonesian Language using FastText and LSTM Algorithms 2

Optimizing Hate Speech Detection in the Indonesian Language ...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Wenando, Febby Apri Yusoff, Nooraini Izrin, Nurul Universitas Andalas Department of Information System Padang Indonesia Universiti Malaysia Kelantan Department of Data Science Kelantan Malaysia Universiti Teknikal Malaysia Department of Software Engineering Malaysia

ISBN: (纸本)9798350389241

The term hate speech encompasses communication that conveys hostility or prejudice toward individuals or groups based on specific attributes, including race, ethnicity, gender, or religion. Given the prevalence of such harmful content on major social media platforms like Twitter, it is imperative to prioritize ongoing research into automated detection methods. This study employs advanced artificial intelligence and machine learning techniques, involving pivotal stages such as data collection, preprocessing, oversampling, fastText word embedding, and classification using Long Short-Term Memory (LSTM) networks to achieve the objective. The amalgamation of fastText embeddings with LSTM classification yielded noteworthy results, with the model achieving 94% accuracy, 92% precision, 94% recall, and a 93% F1 score. These outcomes underscore the effectiveness of sophisticated machine learning methodologies in accurately discerning and addressing the proliferation of hate speech online, particularly in the context of Indonesian-language content. © 2024 IEEE.

关键词： Speech recognition

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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A comparison on the effects of coal fines and sand fouling on the shear behaviors of railway ballast using large scale direct shear tests

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Journal of Traffic and Transportation engineering(English Edition) 2025年第1期12卷 52-67页

作者： Xu Zhang Shunying Ji Yang Feng Yongjun Li Chunfa Zhao School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China State Key Laboratory of Rail Transit Vehicle System Southwest jiaotong UniversityChengdu 610031China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

Railway ballast is easily contaminated by different fouling material and the fouled ballast always causes the deterioration of railway *** previous studies focused on the effect of a certain kind of fouling material on ballast performance,but few studies compared the influence of different fouling *** paper presents a series of direct shear tests on the clean ballast and the fouled ballast mixed with coal fines or sand to understand the effects of different fouling material.A unified empirical model is proposed to well capture the shear stress-displacement responses of the clean and fouled *** equations are also obtained to characterize the variation of the nonlinear shear strength envelope of the fouled ballast with the fouling *** on these equations,a simple method is proposed to predict the shear strength of the fouled ballast using the shear test results of clean *** measured results demonstrate significant differences between the effects of coal fines and sand on the shear behaviors of ballast,to which particular attention should be paid in order to take targeted maintenance measures for the fouled *** mechanisms for the different effects of coal fines and sand are also analyzed to understand the roles that various fouling material plays in the fouled ballast.

关键词： Railway ballast Fouled ballast Shear strength Direct shear test Contamination

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Design and implementation of a standardized review system for gas station safety production based on SpringBoot 2

Design and implementation of a standardized review system fo...

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2nd International Symposium on Computer Applications and Information systems, ISCAIS 2023

作者： Li, Heng Liu, Yuxing School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Shanghai Tansi Computer System Co. Ltd Shanghai China

ISBN: (纸本)9781510666665

Gas stations are flammable and explosive hazardous places, and safety is the first priority. At present, when gas stations are reviewed for production safety standardization, they mainly rely on reviewers to score on site on the production safety standardization review form, and then conduct manual statistical analysis. The development of a standardized production safety review system for gas stations can overcome the problems of high labor intensity, low work efficiency, and general review accuracy caused by the manual mode. The system is developed by using software engineering methods, following the object-oriented development idea, adopting the development mode of separating the front and rear ends, developing the back end based on the SpringBoot framework, developing the front end using the VUE framework, and selecting MySQL 8.0 as the background database. The system mainly includes review index management module, release review task module, implementation review module, and management review result module. The practical application of the system can improve the efficiency and quality of the standardization review of gas station safety production, so as to further improve the safety management level and informatization level of the gas station. © 2023 SPIE.

关键词： Efficiency

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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MPTEM: A Reliable Trust Evaluation Model for Forest IoT system 7

MPTEM: A Reliable Trust Evaluation Model for Forest IoT Syst...

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7th International Conference on Computer and Communication systems, ICCCS 2022

作者： Li, Da Fu, Xingzhen Zan, Yanzhu BeiJing University of Technology BeiJing Engineering Research Center for Iot Software and System Beijing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

With the development of the Internet of Things system, the deployment scenarios of the Internet of Things system become diverse, and how to ensure the safe operation of the Internet of Things system becomes more and more important. In smart forest, environmental monitoring and other projects, people generally use the Internet of Things system to build a forest monitoring network. In order to solve the problem that the forest Internet of Things system is vulnerable to cyber attacks and other factors, which leads to abnormal operation of equipment, this paper proposes a multi-perspective trust evaluation model (MPTEM). The model can fully integrate the characteristics of the forest Internet of Things system, and fuse the trust values from multiple perspectives into the final node trust, thus making the trust evaluation more efficient and accurate. © 2022 IEEE.

关键词： Internet of things

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