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arXiv 2023年

作者： Unterkalmsteiner, Michael Gorschek, Tony Islam, A.K.M. Moinul Cheng, Chow Kian Permadi, Rahadian Bayu Feldt, Robert Software Engineering Research Lab School of Computing Blekinge Institute of Technology KarlskronaSE-371 79 Sweden Software Engineering: Process and Measurement Research Group Department of Computer Science University of Kaiserslautern PO Box 3049 Kaiserslautern67653 Germany General Electrics Healthcare Healthcare IT Munzinger Straße 5 Freiburg79111 Germany Amadeus S. A. S. Product Marketing and Development 485 Route du Pin Montard Boite Postale 69 Sophia Antipolis 06902 Cedex France

BACKGROUND—software Process Improvement (SPI) is a systematic approach to increase the efficiency and effectiveness of a software development organization and to enhance software products. OBJECTIVE—This paper aims to identify and characterize evaluation strategies and measurements used to assess the impact of different SPI initiatives. METHOD—The systematic literature review includes 148 papers published between 1991 and 2008. The selected papers were classified according to SPI initiative, applied evaluation strategies and measurement perspectives. Potential confounding factors interfering with the evaluation of the improvement effort were assessed. RESULTS—Seven distinct evaluation strategies were identified, whereas the most common one, "Pre-Post Comparison", was applied in 49% of the inspected papers. Quality was the most measured attribute (62%), followed by Cost (41%) and Schedule (18%). Looking at measurement perspectives, "Project" represents the majority with 66%. CONCLUSION—The evaluation validity of SPI initiatives is challenged by the scarce consideration of potential confounding factors, particularly given that "Pre-Post Comparison" was identified as the most common evaluation strategy, and the inaccurate descriptions of the evaluation context. Measurements to assess the short and mid-term impact of SPI initiatives prevail, whereas long-term measurements in terms of customer satisfaction and return on investment tend to be less used. © 2023, CC BY-NC-SA.

关键词： Customer satisfaction

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Intelligent Monitoring of IoT Devices using Neural Networks

Intelligent Monitoring of IoT Devices using Neural Networks

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Ashima Chawla Pradeep Babu Trushnesh Gawande Erik Aumayr Paul Jacob Sheila Fallon Software Research Institute Athlone Institute of Technology Athlone Ireland Cloud Pipeline Engineering Workday Dublin Ireland Application Development Platform Ericsson Athlone Ireland Network Management Lab Ericsson Athlone Ireland

ISBN: (数字)9781728177052

ISBN: (纸本)9781728177069

The Internet of Things (IoT) has seen expeditious growth in recent times with 7 billion connected devices in 2020, thus leading to the vital importance of real-time monitoring of IoT devices. Through this paper, we demonstrate the idea of building a cloud-native application to monitor smart home devices. The application intends to provide valuable performance metrics from the perspective of end-users and react to anomalies in real-time. In this demo paper, we conduct the demonstration using Autoencoder (an unsupervised technique) based Deep Neural Networks (DNNs) to learn the normal operating conditions of power consumption of smart devices. When an anomaly is detected, the DNNs take proactive action and send appropriate commands back to the device. In addition, the users are provided with a real-time graphical representation of power consumption. This will help to save electricity on a domestic as well as industrial level. Finally, we discuss the future prospects of monitoring IoT devices.

关键词： Cloud computing Technological innovation Power demand Neural networks Real-time systems Internet of Things Monitoring

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A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

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Computers, Materials & Continua 2021年第3期66卷 3289-3310页

作者： Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Salama A.Mostafa Mashael S.Maashi Alaa S.Al-Waisy Mohammed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq eVIDA Lab University of DeustoBilbao48007Spain Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Pure Science Department Ministry of EducationGeneral Directorate of CurriculaBaghdad10Iraq Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global *** biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 *** this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 ***(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection *** on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 *** comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

关键词： Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning

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Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images

arXiv

引用

arXiv 2023年

作者： Song, Zhiyun Du, Penghui Yan, Junpeng Li, Kailu Shou, Jianzhong Lai, Maode Fan, Yubo Xu, Yan The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Urology National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Chaoyang District Beijing100021 China The Department of Pathology School of Medicine Zhejiang University Zhejiang Provincial Key Lab Disease Proteomics Alibaba-Zhejiang University Joint Research Center of Future Healthcare Hangzhou310053 China

Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the extraction of nucleus-level information, which is essential for pathologic analysis. In this work, we propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation between histopathology images and pseudo mask images. The generation process is modulated by both conditional and stochastic style representations, ensuring the reality and diversity of the generated histopathology images for pretraining. Further, an instance segmentation guided strategy is employed to capture instance-level information. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks with the transfer learning protocol, and yields superior results than other self-supervised approaches on 8 semi-supervised tasks. Our project is publicly available at https://***/zhiyuns/UNITPathSSL. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Image super-resolution via dynamic network

arXiv

引用

arXiv 2023年

作者： Tian, Chunwei Zhang, Xuanyu Zhang, Qi Yang, Mingming Ju, Zhaojie School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China Research & Development Institute Northwestern Polytechnical University Shenzhen China School of Economics and Management Harbin Institute of Technology at Weihai Weihai264209 China Tencent AI lab Shenzhen China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

Convolutional neural networks depend on deep network architectures to extract accurate information for image super-resolution. However, obtained information of these convolutional neural networks cannot completely express predicted high-quality images for complex scenes. In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block. The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super-resolution. To enhance robustness of obtained super-resolution model for complex scenes, a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super-resolution model for varying scenes. To prevent interference of components in a wide enhancement block, a refinement block utilizes a stacked architecture to accurately learn obtained features. Also, a residual learning operation is embedded in the refinement block to prevent long-term dependency problem. Finally, a construction block is responsible for reconstructing high-quality images. Designed heterogeneous architecture can not only facilitate richer structural information, but also be lightweight, which is suitable for mobile digital devices. Experimental results show that our method is more competitive in terms of performance, recovering time of image super-resolution and complexity. The code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Spectro-ViT: A Vision Transformer Model for GABA-edited MRS Reconstruction Using Spectrograms

arXiv

引用

arXiv 2023年

作者： Dias, Gabriel Berto, Rodrigo Pommot Oliveira, Mateus Ueda, Lucas Dertkigil, Sergio Costa, Paula D.P. Shamaei, Amirmohammad Souza, Roberto Harris, Ashley Rittner, Leticia School of Electrical and Computer Engineering University of Campinas Campinas Brazil Department of Biomedical Engineering University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute Calgary Canada Research and Development Center in Telecommunications CPQD Campinas Brazil School of Medical Sciences University of Campinas Campinas Brazil Artificial Intelligence Lab. Recod.ai University of Campinas Campinas Brazil Department of Electrical and Software Engineering University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada ICT 352C 2500 University Dr NW CalgaryABT2N 1N4 Canada

Purpose: To investigate the use of a Vision Transformer (ViT) to reconstruct/denoise GABA-edited magnetic resonance spectroscopy (MRS) from a quarter of the typically acquired number of transients using spectrograms. Theory and Methods: A quarter of the typically acquired number of transients collected in GABA-edited MRS scans are pre-processed and converted to a spectrogram image representation using the Short-Time Fourier Transform (STFT). The image representation of the data allows the adaptation of a pre-trained ViT for reconstructing GABA-edited MRS spectra (Spectro-ViT). The Spectro-ViT is fine-tuned and then tested using in vivo GABA-edited MRS data. The Spectro-ViT performance is compared against other models in the literature using spectral quality metrics and estimated metabolite concentration values. Results: The Spectro-ViT model significantly outperformed all other models in four out of five quantitative metrics (mean squared error, shape score, GABA+/water fit error, and full width at half maximum). The metabolite concentrations estimated (GABA+/water, GABA+/Cr, and Glx/water) were consistent with the metabolite concentrations estimated using typical GABA-edited MRS scans reconstructed with the full amount of typically collected transients. Conclusion: The proposed Spectro-ViT model achieved state-of-the-art results in reconstructing GABA-edited MRS, and the results indicate these scans could be up to four times faster. © 2023, CC BY.

关键词： Deep learning

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Transformers in Medical Image Analysis: A Review

arXiv

引用

arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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