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arXiv 2023年

作者： Khan, Arif Ali Khan, Javed Ali Akbar, Muhammad Azeem Zhou, Peng Fahmideh, Mahdi M3S Empirical Software Engineering Research Unit University of Oulu OuluFI-90014 Finland Department of Software Engineering University of Science and Technology Bannu28100 Pakistan Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Business University of Southern Queensland ToowoombaQLD4350 Australia

Context: software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q&A forums. Objective: We conducted an empirical study to analyze developers’ discussions on Q&A forums to gain insights into software development approaches in practice. Method: We analyzed 13,903 developers’ posts across Stack Overflow (SO), software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings: We identified 15 frequently mentioned software development approaches topics on Q&A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions: The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop. © 2023, CC BY.

关键词： Project management

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Circularly Deformable Medical Image Registration Based on Transformer-CNN with Prompt

SSRN

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SSRN 2024年

作者： Li, Longhao Li, Li Zhang, Yunfeng Bao, Fangxun Yao, Xunxiang Zhang, Caiming School of Computer Science and Technology Shandong University of Finance and Economics Shandong Jinan250014 China School of Mathmatics Shandong University Shandong Jinan250100 China School of Software Shandong University Shandong Jinan250101 China Shandong Co-Innovation Center of Future Intelligent Computing Shandong Yantai264025 China

Medical image registration is a fundamental task in medical image analysis. Recently, competitive methods have been proposed, such as deep learning-based registration and deformable registration, which have demonstrated promising results. However, meeting the demands for high precision in clinical applications is still a challenge. Here, we propose a cyclic optimization registration framework that deeply fuses diffeomorphic and deep learning methods through a single forwardtwo path structure. Neural network estimates the initial deformation field, which directly generates registered images to enhance feature extraction capabilities. Additionally, dynamic diffeomorphism is introduced for the initial deformation field to generate the final deformation field, ensuring the invertibility of the transformation. We incorporate the Dense Spatial Correspondence Prompt module for cyclically learning the final deformation field, enabling the network to estimate smoother and more accurate spatial transformations. We conducted experiments on a publicly available 3D dataset, and exceptional registration accuracy was achieved by our method while preserving desirable diffeomorphic properties. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Exploit your Latents: Coarse-Grained Protein Backmapping with Latent Diffusion Models 39

Exploit your Latents: Coarse-Grained Protein Backmapping wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Rongchao Huang, Yu Lou, Yiwei Xin, Yi Chen, Haixu Cao, Yongzhi Wang, Hanpin Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute of Geriatrics National Clinical Research Center of Geriatrics Disease Chinese PLA General Hospital Beijing China

ISBN: (纸本)157735897X

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory

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computers, Materials & Continua 2020年第6期63卷 1471-1483页

作者： Yongjun Ren Jian Qi Yaping Cheng Jin Wang Osama Alfarraj School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action ***,the technologies of the digital continuity guarantee are still *** first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic ***,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic ***,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.

关键词： Electronic record digital continuity data quality

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Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach

arXiv

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arXiv 2024年

作者： Jiang, Feihu Qin, Chuan Zhang, Jingshuai Yao, Kaichun Chen, Xi Shen, Dazhong Zhu, Chen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University China Institute of Software Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China The Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute China

In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU. © 2024, CC BY.

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Enhanced Assessment of Auditory Perception Function in Disorders of Consciousness Using a Minimalist Wearable Brain-computer Interface

Enhanced Assessment of Auditory Perception Function in Disor...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Chenxin Xue Jun Xiao Huang Wu Li Hu Jiahui Pan Yuanqing Li School of Automation Science and Engineering South China University of Technology Guangzhou China School of Electric Power Engineering South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

The Coma Recovery Scale-Revised (CRS-R) is a clinical behavioral method for assessing the levels of consciousness and perceptual abilities in patients with Disorders of Consciousness (DOC). However, due to severe impairment of motor functions in DOC patients and subjective factors of clinical evaluators, the misdiagnosis rate of the CRS-R is very high. Brain-computer interfaces (BCIs) can directly detect patients’ brain activity in response to external stimuli, independent of patients’ behavioral representation, thereby achieving a more objective assessment. In this study, we introduced a minimalist wearable BCI system grounded in the auditory oddball paradigm to enhance assessment of auditory perception function in DOC patients. In the experiments, sinusoidal tones and the complex sound of clapping were selected as standard stimuli occurring frequently and occasional deviant stimuli, respectively. The specific event-related potentials evoked by the auditory stimuli facilitated the assessment of auditory perceptual function. The experimental results indicate that the DOC patients with auditory perception functions can also be detected by the BCI system. As a result, the BCI could serve as a valuable adjunct tool in clinical practice of the CRS-R.

关键词： Brain External stimuli Medical services Big Data Motors Brain-computer interfaces Electroencephalography

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Correction to: Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

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Neural Processing Letters 2022年第1期55卷 33-34页

作者： Baskar, M. Renuka Devi, R. Ramkumar, J. Kalyanasundaram, P. Suchithra, M. Amutha, B. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram India Department of Software Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram India Department of Wireless Sensor Networks Institute of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

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Self-paced consensus clustering with bipartite graph 29

Self-paced consensus clustering with bipartite graph

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhou, Peng Du, Liang Li, Xuejun School of Computer Science and Technology Anhui University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computer and Information Technology Shanxi University China

ISBN: (纸本)9780999241165

Consensus clustering provides a framework to ensemble multiple clustering results to obtain a consensus and robust result. Most existing consensus clustering methods usually apply all data to ensemble learning, whereas ignoring the side effects caused by some difficult or unreliable instances. To tackle this problem, we propose a novel self-paced consensus clustering method to gradually involve instances from more reliable to less reliable ones into the ensemble learning. We first construct an initial bipartite graph from the multiple base clustering results, where the nodes represent the instances and clusters and the edges indicate that an instance belongs to a cluster. Then, we learn a structured bipartite graph from the initial one by self-paced learning, i.e., we automatically decide the reliability of each edge and involves the edges into graph learning in order of their reliability. At last, we obtain the final consensus clustering result from the learned bipartite graph. The extensive experimental results demonstrate the effectiveness and superiority of the proposed method. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Risk Region-based Prediction Model for the Epidemic Spreading

Risk Region-based Prediction Model for the Epidemic Spreadin...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Fuwei Li Dianjie Lu Baoxu An Guijuan Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Studying the spread process of epidemics in the crowd is a significant topic, which can help to take measures in advance to avoid epidemic spread and reduce the losses of life and property of the general public. However, this is a challenging problem that how to capture the spatio-temporal characteristics of the epidemic spreading. In this paper, we propose a risk region-based prediction model to characterize the spread feature. First, we propose a risk region-based spread model to describe the spatial relationship between individuals by dividing risk regions and assigning different spread characteristics to different risk regions. Second, we develop a spatio-temporal point process-based infection intensity quantification method to describe the variation of epidemic spread characteristics over time in different risk regions in the form of intensity. Then, we propose a long short-term memory (LSTM) based intensity function prediction method to solve the corresponding intensity and predict the spread process by the spatio-temporal point process (STPP). Finally, we implement an epidemic spreading simulation platform to verify our method and visualize the experimental results. The proposed model is expected to provide guidance for predicting the spread of epidemics.

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PANDA: Parkinson’s Assistance and Notification Driving Aid

arXiv

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arXiv 2025年

作者： Wen, Tianyang Zhang, Xucheng Wan, Zhirong Zhao, Jing Zhu, Yicheng Su, Ning Peng, Xiaolan Huang, Jin Sun, Wei Tian, Feng Li, Franklin Mingzhe Institute of Software Chinese Academy of Sciences China Aerospace Center Hospital China Peking Union Medical College Hospital China Carnegie Mellon University United States School of Computer Science and Technology University of Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

Parkinson’s Disease (PD) significantly impacts driving abilities, often leading to early driving cessation or accidents due to reduced motor control and increasing reaction times. To diminish the impact of these symptoms, we developed PANDA (Parkinson’s Assistance and Notification Driving Aid), a multi-modality real-time alert system designed to monitor driving patterns continuously and provide immediate alerts for irregular driving behaviors, enhancing driver safety of individuals with PD. The system was developed through a participatory design process with 9 people with PD and 13 non-PD individuals using a driving simulator, which allowed us to identify critical design characteristics and collect detailed data on driving behavior. A user study involving individuals with PD evaluated the effectiveness of PANDA, exploring optimal strategies for delivering alerts and ensuring they are timely and helpful. Our findings demonstrate that PANDA has the potential to enhance the driving safety of individuals with PD, offering a valuable tool for maintaining independence and confidence behind the wheel. © 2025, CC BY.

关键词： Neurodegenerative diseases

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