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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation Algorithm for Global Optimization and Feature Selection

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Journal of Bionic Engineering 2023年第5期20卷 2416-2442页

作者： Hanyu Hu Weifeng Shan Jun Chen Lili Xing Ali Asghar Heidari Huiling Chen Xinxin He Maofa Wang School of Emergency Management Institute of Disaster PreventionSanhe065201China Earthquake Administration of Anhui Province Hefei230031China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional *** Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of ***-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient *** paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the ***,a binary version of DCFBI(BDCFBI)is applied to *** conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search *** influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied *** is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary *** results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.

关键词： Feature selection Forensic-based investigation algorithm Crisscross mechanism Global optimization Metaheuristic algorithms Bionic algorithm

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From Chart to QA Pairs: A Context-Aware Generation Framework for Chart-Containing Documents 25

From Chart to QA Pairs: A Context-Aware Generation Framework...

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Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security

作者： Qianwen Shen Zeyu Ma Mingang Chen Computer and Information Engineering College Shanghai Polytechnic University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Mathematical and Science College Shanghai Normal University Shanghai China

ISBN: (纸本)9798400713453

While Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities across various general tasks, developing domain-specific model remains an urgent need. This requires fine-tuning with high-quality data. Extraction of question-answer (QA) pairs from domain knowledge documents is a crucial prerequisite for fine-tuning, especially when these documents containing complex charts and unstructured data. This paper introduces a framework for extracting QA pairs from charts, which combines OCR and VLM to fully leverage contextual information in the documents and generate deep, contextually relevant questions. Our method first uses OCR for initial chart recognition, followed by a VLM-based “Caption-Reflection'' paradigm to reduce misrecognition. We then design a context localization module which combines local and global contexts to generate relevant QA pairs. Additionally, we construct a specialized chart knowledge base to guide the generation of QA pairs. Experimental results show significant improvement in both chart recognition performance and QA pairs generation quality: the average precision increases from 0.52 to 0.90, the average F1 Score improves from 0.64 to 0.94, the average FPR decreases from 0.55 to 0 respectively. The generated QA pairs perform well in terms of statistical relevance and contextual consistency.

关键词： Artificial Intelligence

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DAGC: Data-Aware Adaptive Gradient Compression 42

DAGC: Data-Aware Adaptive Gradient Compression

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Lu, Rongwei Song, Jiajun Chen, Bin Cui, Laizhong Wang, Zhi Tsinghua Shenzhen International Graduate School Tsinghua-Berkeley Shenzhen Institute China Dalian University of Technology School of Software Technology China Harbin Institute of Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering China China Peng Cheng Laboratory China

ISBN: (纸本)9798350334142

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers;This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication. © 2023 IEEE.

关键词： Nonlinear programming

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A Neural Architecture Search Method with Multi-Dimensional Correlation Representation for Fault Diagnosis 9

A Neural Architecture Search Method with Multi-Dimensional C...

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9th IEEE Smart World Congress, SWC 2023

作者： Liu, Yumeng Wang, Tingqi Zheng, Xu Tian, Ling Wang, Hongan Institute of Software Chinese Academy of Sciences Beijing Key Laboratory of Human-Computer Interaction Beijing China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350319804

As deep learning has become more widely used for fault diagnosis, the shortcomings of model transferability and human model design costs are growing increasingly evident. The current work has tackled each of these two issues. Domain adaptation methods can provide transportability by mapping data to a common domain. For the consumption of manually constructed networks, Neural Architecture Search (NAS) is an intelligent solution that automatically select the ideal network structure. Unfortunately, there is little work to connect these two methodologies in fault diagnosis. Furthermore, these approaches are incapable in extracting internal correlations from instrumental signal data. In this paper, we propose a transferrable NAS fault diagnosis method, which maximizes savings in manual costs. We also integrate graph neural networks (GNN) in a stepwise manner into the NAS and domain adaptation to provide a multi-dimension correlation representation. Firstly, we structure the signal data into graph based on temporal relationships. Then, NAS designs automatically for selecting the optimal network structure and useful features. In NAS, graph encoder of GNN is used to establish internal relationships. Finally, domain adaptation provides transferability. At the same time, GNN's weights in domain adaptation help to further mine data internal associations. We tested our method on two publicly available datasets and performed comparisons. The experimental results show the proposed method has superior performance. © 2023 IEEE.

关键词： Domain Adaptation Fault Diagnosis Graph Neural Network Neural Architecture Search

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy

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Frontiers of Information technology & Electronic Engineering 2023年第10期24卷 1375-1389页

作者： Zhen LIANG Taoran WU Wanwei LIU Bai XUE Wenjing YANG Ji WANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China State Key Laboratory of Computer Science Institute of Software Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Key Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China

Robustness of deep neural networks(DNNs)has caused great concerns in the academic and industrial communities,especially in safety-critical *** of verifying whether the robustness property holds or not in certain neural networks,this paper focuses on training robust neural networks with respect to given ***-of-the-art training methods,interval bound propagation(IBP)and CROWN-IBP,perform well with respect to small perturbations,but their performance declines significantly in large perturbation cases,which is termed“drawdown risk”in this ***,drawdown risk refers to the phenomenon that IBPfamily training methods cannot provide expected robust neural networks in larger perturbation cases,as in smaller perturbation *** alleviate the unexpected drawdown risk,we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch(global robustness training),and the corresponding robustness losses are combined with monotonically decreasing weights(monotonically decreasing robustness training).With experimental demonstrations,our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great *** is also noteworthy that our training method achieves higher model accuracy than the original training methods,which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

关键词： Robust neural networks Training method Drawdown risk Global robustness training Monotonically decreasing robustness

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Pronounced advance on typhoon track forecast with global convection-permitting model

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Science Bulletin 2025年第9期70卷 1424-1428页

作者： Gu, Jun Zhao, Chun Li, Gudongze Feng, Jiawang Xu, Mingyue Du, Qiuyan Xia, Zihan Li, Yubin Chen, Guanghua Hao, Xiaoyu Chen, Junshi An, Hong Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of China Hefei230026 China Laoshan Laboratory Qingdao266237 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

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Towards Defect Prediction for Quantum software*

Towards Defect Prediction for Quantum Software*

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Quantum software Engineering (Q-SE), International Workshop on

作者： Xuan Mao Zijie Huang Jianxin Ge Chao Wang Wuxu Wang Lizhi Cai Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798331526214

ISBN: (纸本)9798331526221

software defects are faults or bugs within a program that can lead to incorrect or unexpected outcomes. Efficiently allocating software quality assurance (SQA) resources to components with a higher likelihood of defects, based on software defect prediction (SDP) models, can save significant effort. Although SDP has been extensively studied in classical software, its applicability to quantum software remains unexplored. Defect prediction for quantum software presents unique challenges, including the susceptibility to quantum-specific defects arising from quantum coding conventions and the limited size of available datasets. To address these issues, we propose QDP-FSL, an SDP model using a pre-trained code model to capture the semantics of quantum software code, and applying few-shot learning (FSL) to learn effectively from a small number of defective samples. Results show that QDP-FSL outperforms baseline methods that rely on static analysis. This work lays the foundation for future research in defect prediction for quantum software engineering and outlines potential directions for further improvement.

关键词： Codes Conferences Semantics computer bugs software quality Static analysis Predictive models Encoding Few shot learning software engineering

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Towards Early Warning and Migration of High-Risk Dormant Open-Source software Dependencies

Towards Early Warning and Migration of High-Risk Dormant Ope...

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IEEE/ACM International Conference on software Engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Zijie Huang Lizhi Cai Xuan Mao Kang Yang Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

ISBN: (数字)9798331537111

ISBN: (纸本)9798331537128

Dormant open-source software (OSS) dependencies are no longer maintained or actively developed, their related code components are more vulnerable and error-prone since they can hardly keep up with evolving software dependents. Presently, their migration remains costly and challenging for practitioners. To tackle such a challenge, we intend to characterize, predict, and automatically migrate high-risk dormant OSS dependencies. Our pilot study of 4,945 Maven dependencies reveals over half of them are dormant, and 12.15% pose a high security risk. These high-risk dependencies can be predicted early based on their version release and usage characteristics. They are rarely migrated by developers, and simple one-to-one API migrations can be achieved with little context using Large Language Models (LLMs). Future research will be conducted on a more complete dataset, incorporate socio-technical features for improved high-risk prediction, and fine-tune a migration code generator.

关键词： Analytical models Codes Large language models Supply chains Predictive models Security Sustainable development Open source software software engineering Context modeling

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Graph-in-Graph Convolutional Network for Hyperspectral Image Classification

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 1157-1171页

作者： Jia, Sen Jiang, Shuguo Zhang, Shuyu Xu, Meng Jia, Xiuping College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Key Laboratory for Geo-Environmental Monitoring of Coastal Zone Shenzhen University Ministry of Natural Resources Shenzhen518060 China School of Engineering and Information Technology University of New South Wales CanberraACT 2612 Australia

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation. To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG representation covers information inside and outside superpixels, respectively, corresponding to the local and global characteristics of ground objects. Concretely, after segmenting HSI into disjoint superpixels, each one is converted to an internal graph. Meanwhile, an external graph is constructed according to the spatial adjacent relationships among superpixels. Significantly, each node in the external graph embeds a corresponding internal graph, forming the so-called GiG structure. Then, GiGCN composed of internal and External graph convolution (EGC) is designed to extract hierarchical features and integrate them into multiple scales, improving the discriminability of GiGCN. Ensemble learning is incorporated to further boost the robustness of GiGCN. It is worth noting that we are the first to propose the GiG framework from the superpixel point and the GiGCN scheme for HSI classification. Experiment results on four benchmark datasets demonstrate that our proposed method is effective and feasible for HSI classification with limited labeled samples. For study replication, the code developed for this study is available at https://***/ShuGuoJ/***. © 2012 IEEE.

关键词： Convolution

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