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IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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Gradient Boosting-Accelerated Evolution for Multiple-Fault Diagnosis

Gradient Boosting-Accelerated Evolution for Multiple-Fault D...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Hongfei Wang Chenliang Luo Deqing Zou Hai Jin Wenjie Cai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China College of Public Administration Wuhan China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Logic diagnosis is a key step in yield learning. Multiple faults diagnosis is challenging because of several reasons, including error masking, fault reinforcement, and huge search space for possible fault combinations. This work proposes a two-phase method for multiple-fault diagnosis. The first phase efficiently reduces the potential number of fault candidates through machine learning. The second phase obtains the final diagnosis results, by formulating the task as an combinational optimization problem that is later iteratively solved using binary evolution computation. Experiments shows that our method outperforms two existing methods for multiple-fault diagnosis, and achieves better diagnosability (improved by $1.87\times$ ) and resolution (improved by $1.42\times$ ) compared with a state-of-the-art commercial diagnosis tool.

关键词： Fault diagnosis Adaptation models Computational modeling Fitting Filtering algorithms Boosting Computational efficiency

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Evolving temporal convolutional neural networks for modeling turntable servo systems

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Applied Soft Computing 2025年 172卷

作者： Xie, Cheng Xue, Bing Zhang, Mengjie Chen, Songlin Liu, Yang Control and Simulation Center School of Astronautics Harbin Institute of Technology Heilongjiang Harbin150001 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China

Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo performance, some advanced model-based control methods have been used recently, but the control performance is greatly influenced by the accuracy of the model. Therefore, this paper proposes evolving temporal convolutional networks (TCNs) for accurately modeling turntable servo systems. Considering the advantages of TCNs in capturing local dependencies in sequence data and parallel computing, a TCN is adopted to compensate the unmodeled dynamics to significantly improve the accuracy of the existing dynamics model. Then, an evolutionary algorithm (EA) based architecture optimization algorithm for the above TCN model is proposed, and the network performance is continuously improved by simulating the population evolution in nature. Finally, the weight inheritance method is utilized to evaluate the performance of individuals during the evolution process to effectively improve the execution efficiency of the whole algorithm. In addition, the subnets inherited from the supernet meet the real-time requirements of the actual tasks by setting real-time constraints on the supernet. The experimental results demonstrate the superiority of the proposed model against the peer competitors in terms of the prediction performance. In addition, the proposed optimization algorithm also shows higher execution efficiency as verified by ablation experiments. © 2025 Elsevier B.V.

关键词： Evolutionary algorithms (EAs) System identification and modeling Temporal convolutional networks (TCNs) Turntable servo systems

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Online and Offline Hybrid Teaching Mode Based on Scenario Cases: A Case Study of MySQL databases 4

Online and Offline Hybrid Teaching Mode Based on Scenario Ca...

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4th International Conference on computer science and Educational Informatization, CSEI 2022

作者： You, Jinguo Wang, Ziwen Zhao, Xiaoxia Kou, Weili Zhang, Yunwei Zhang, Ting Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Computer Center Kunming University of Science and Technology Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Kunming China School of Information Engineering Jiaozuo University Henan Jiaozuo China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Teaching mode design is a process to achieve the learning goal. Under the background of information technology, online and offline hybrid teaching mode has become the new form of education, and the closely related teaching mode design plays an essential role in the success of teaching implementation. Scenario case teaching can stimulate students' interest in learning and improve their ability to analyze and solve problems. Taking the MySQL database course as an example, this paper introduces the design and implementation process of online and offline hybrid teaching modes based on scenario cases. The practical results demonstrate that the hybrid teaching mode far outperforms the traditional teaching effect in terms of the aspects of students' basic knowledge of databases, practical operation level, and self-learning ability. Furthermore, this teaching mode has gained popularity in the studying of both our university and league universities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Design

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STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer

STFormer: Learning to Explore Spot Relationships for Spatial...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Gan Zhan Xiuju Du Jing Liu Yinhao Li Lanfen Lin Jingsong Li Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies.

关键词： Deep learning Costs Correlation Histopathology Transcriptomics Colorectal cancer Transformers Gene expression Tumors Biomedical imaging

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Feature Rotation Invariance Learning for Point Cloud Analysis 10

Feature Rotation Invariance Learning for Point Cloud Analysi...

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10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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An ai-Human Collaboration Chatbot with Digital Representation of Users for Knowledge Sharing

An AI-Human Collaboration Chatbot with Digital Representatio...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Sruthy Anand Adhwaith B Pillai A M Abhishek Sai Sethuraman N Rao Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Software Development Engineer Fridayy AI Noida India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

With the advancement in Artificial Intelligence (ai), especially generative ai, conversational agents are becoming a norm in everyday lives. These agents can become more intelligent if they can collaborate with humans or other agents in analysing the conversation with the user. This work presents the use of ai to connect different sets of users and help them solve their life problems. Currently, sharing the wisdom of the old generation is happening within the close family or the student circle alone. To receive knowledge from a wider community at their fingertips, we designed a chatbot called wisdomBOT which can connect the younger generation users with the older generation users to receive their wisdom through the sharing of life experiences and stories and to learn from them. It also helps to address the loneliness of the older generation and connect them through a new way to make younger friends.

关键词： Pervasive computing Sentiment analysis Social networking (online) Generative ai Digital representation Collaboration Oral communication Chatbots Security Older adults

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

arXiv

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arXiv 2024年

作者： Takayama, Masayuki Okuda, Tadahisa Pham, Thong Ikenoue, Tatsuyoshi Fukuma, Shingo Shimizu, Shohei Sannai, Akiyoshi Data Science and AI Innovation Research Promotion Center Shiga University Japan Department of Health Data Science Tokyo Medical University Graduate School of Medicine Human Health Sciences Kyoto University Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Japan Graduate School of Medicine Human Health Sciences Kyoto University Department of Epidemiology Infectious Disease Control and Prevention Hiroshima University Graduate school of Biomedical and Health Sciences Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Institute for the Advanced Study of Human Biology Kyoto University Center for Advanced Intelligence Project RIKEN Japan Department of Physics Kyoto University Data Science and AI Innovation Research Promotion Center Shiga University Graduate School of Engineering The University of Tokyo Center for Advanced Intelligence Project RIKEN Research Development Center for Large Language Models National Institute of Informatics Japan

In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is significant for creating consistent meaningful causal models, despite the challenges in systematic acquisition of the background knowledge. To overcome these challenges, this paper proposes a novel methodology for causal inference, in which SCD methods and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through "statistical causal prompting (SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result with prior knowledge from LLM-KBCI to approach the ground truth, and that the SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, by using an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve SCD on this dataset, even if this dataset has never been included in the training data of the LLM. The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains. © 2024, CC BY.

关键词： Computational linguistics

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Multi-Modal CLIP-Informed Protein Editing

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Health data science 2024年第1期4卷 1-12页

作者： Mingze Yin Hanjing Zhou Yiheng Zhu Miao Lin Yixuan Wu Jialu Wu Hongxia Xu Chang-Yu Hsieh Tingjun Hou Jintai Chen Jian Wu School of Medicine Zhejiang UniversityHangzhouChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Medical Big Data Center Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical SciencesZhejiang UniversityHangzhouChina AI Thrust Information HubHKUST(Guangzhou)GuangzhouChina Second Affiliated Hospital School of Medicine HangzhouChina School of Public Health Zhejiang UniversityHangzhouChina Institute of Wenzhou WenzhouChina

Background:Proteins govern most biological functions essential for life,and achieving controllable protein editing has made great advances in probing natural systems,creating therapeutic conjugates,and generating novel protein ***,machine learning-assisted protein editing(MLPE)has shown promise in accelerating optimization cycles and reducing experimental ***,current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions,limiting their interactivity with human ***:To fill these gaps,we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality *** approach comprises 2 stages:In the pretraining stage,contrastive learning aligns protein-biotext representations encoded by 2 large language models(LLMs).Subsequently,during the protein editing stage,the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein ***:Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains,including enzyme catalytic activity,protein stability,and antibody-specific binding *** improves the state-of-the-art results by a large margin,leading to substantial stability improvements of 16.67%and 16.90%.Conclusions:This capability positions ProtET to advance real-world artificial protein editing,potentially addressing unmet academic,industrial,and clinical needs.

关键词： generating editing fused

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