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information system for Screening and Automation of Document Management in Oncological Clinics

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Open Bioinformatics Journal 2021年第1期14卷 39-50页

作者： Sheketa, Vasyl Pasieka, Mykola Chupakhina, Svitlana Pasieka, Nadiia Ketsyk-Zinchenko, Uliana Romanyshyn, Yulia Yanyshyn, Olha Institute of Information Technologies Software Engineering National Technical University of Oil & Gas Ivano-Frankivsk Ukraine Department of Theory and Methods of Preschool and Special Education Vasyl Stefanyk Precarpathian National University Ivano-Frankivsk Ukraine

Introduction: Automation of business documentation workflow in medical practice substantially accelerates and improves the process and results in better service development. Methods: Efficient use of databases, data banks, and document-oriented storage (warehouses data), including dual-purpose databases, enables performing specific actions, such as adding records, introducing changes into them, performing an either ordinary or analytical search of data, as well as their efficient processing. With the focus on achieving interaction between the distributed and heterogeneous applications and the devices belonging to the independent organizations, the specialized medical client application has been developed, as a result of which the quantity and quality of information streams of data, which can be essential for effective treatment of patients with breast cancer, have increased. Results: The application has been developed, allowing automating the management of patient records, taking into account the needs of medical staff, especially in managing patients’ appointments and creating patient’s medical records in accordance with the international standards currently in force. This work is the basis for the smoother integration of medical records and genomics data to achieve better prevention, diagnosis, prediction, and treatment of breast cancer (oncology). Conclusion: Since relevant standards upgrade the functioning of health care information technology and the quality and safety of patient’s care, we have accomplished the global architectural scheme of the specific medical automation system through harmonizing the medical services specified by the HL7 international. © 2021 Sheketa et al.

关键词： Automation of documentation flow HL7 international Medical records Oncology clinic software applications Warehouses data

T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

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arXiv 2024年

作者： Sun, Chuxiong Zang, Zehua Li, Jiabao Li, Jiangmeng Xu, Xiao Wang, Rui Zheng, Changwen Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China State Key Laboratory of Intelligent Game China University of Chinese Academy of Sciences China School of Automation and Electrical Engineering University of Science and Technology Beijing China

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impact the communication efficiency. Furthermore, existing works often resort to basic mechanisms to integrate observed and received information, impairing the learning process. To tackle these difficulties, we propose Targeted and Trusted Multi-Agent Communication (T2MAC), a straightforward yet effective method that enables agents to learn selective engagement and evidence-driven integration. With T2MAC, agents have the capability to craft individualized messages, pinpoint ideal communication windows, and engage with reliable partners, thereby refining communication efficiency. Following the reception of messages, the agents integrate information observed and received from different sources at an evidence level. This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors. We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the state-of-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Behavioral research

SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

Analytical Models for QoT Estimations in Super-C and Super-C + L Bands Optical Transmission systems

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Analytical Models for QoT Estimations in Super-C and Super-C...

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Yan, Baoluo Sun, Yijun Zhou, Ziwen Liu, Hong Wang, Hongya Huan, Chen Zhao, Zhiyong Feng, Lipeng Zhang, Anxu Feng, Zhenhua Shi, Hu WDM System Department Wireline Product R&D Institute ZTE Corp. Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology 518055 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optical and Electronic Information Wuhan National Laboratory for Optoelectronics Optics Valley Laboratory Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Beijing102209 China

We propose a novel physical model-based QoT estimation with achieved OSNR estimation error 1.5 dB in primary paths and OSNR prediction error 1.76 dB in unestablished lightpaths for both super-C bands and super-C+L ban... 详细信息

ISBN: (纸本)9781957171258

关键词： Light transmission

Nighthawk: Fully Automated Localizing UI Display Issues via Visual Understanding

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arXiv 2022年

作者： Liu, Zhe Chen, Chunyang Wang, Junjie Huang, Yuekai Hu, Jun Wang, Qing The Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China The Monash University Melbourne Australia The Laboratory for Internet Software Technologies State Key Laboratory of Computer Sciences Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

Graphical User Interface (GUI) provides a visual bridge between a software application and end users, through which they can interact with each other. With the upgrading of mobile devices and the development of aesthetics, the visual effects of the GUI are more and more attracting, and users pay more attention to the accessibility and usability of applications. However, such GUI complexity posts a great challenge to the GUI implementation. According to our pilot study of crowdtesting bug reports, display issues such as text overlap, component occlusion, missing image always occur during GUI rendering on different devices due to the software or hardware compatibility. They negatively influence the app usability, resulting in poor user experience. To detect these issues, we propose a fully automated approach, Nighthawk, based on deep learning for modelling visual information of the GUI screenshot. Nighthawk can detect GUIs with display issues and also locate the detailed region of the issue in the given GUI for guiding developers to fix the bug. At the same time, training the model needs a large amount of labeled buggy screenshots, which requires considerable manual effort to prepare them. We therefore propose a heuristic-based training data auto-generation method to automatically generate the labeled training data. The evaluation demonstrates that our Nighthawk can achieve average 0.84 precision and 0.84 recall in detecting UI display issues, average 0.59 AP and 0.60 AR in localizing these issues. We also evaluate Nighthawk with popular Android apps on Google Play and F-Droid, and successfully uncover 151 previously-undetected UI display issues with 75 of them being confirmed or fixed so far. © 2022, CC0.

关键词： Application programs

Capturing features of hourly-resolution energy models through statistical annual indicators

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Capturing features of hourly-resolution energy models throug...

作者： Parrado-Hernando, Gonzalo Herc, Luka Pfeifer, Antun Capellán-Perez, Iñigo Batas Bjelić, Ilija Duić, Neven Frechoso-Escudero, Fernando Miguel González, Luis Javier Gjorgievski, Vladimir Z Department of System Engineering and Automation School of Industrial Engineering University of Valladolid Spain Department of Energy Power Engineering and Environment Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia Institute of Technical Sciences of Serbian Academy of Sciences and Arts Belgrade Serbia Department of Electrical Engineering School of Industrial Engineering University of Valladolid Spain Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University in Skopje Macedonia

Long term-energy planning has gradually moved towards finer temporal and spatial resolutions of the energy system to design the decarbonization of the society. However, integrated assessment models (IAMs), focusing on a broader concept of sustainability transition, are typically yearly-resolution models which complicates capturing the specific supply-demand dynamics, relevant in the transition towards renewable energy sources (RES). Different methods for introducing sub-annual information are being used in IAMs, but the hourly representation of variable RES remains challenging. This article presents a method to translate the main dynamics of an hourly-resolution energy model into a yearly-resolution model. Here we test our method with the current European Union region (EU-27) by configuring and applying the hourly-resolution EnergyPLAN. Multiple linear regression analysis is applied to 174960 simulations (set by varying 39 inputs by clusters and reaching 100% renewable systems), relating the adjusted capacity factors of the technologies as well as the variation of electricity demand and natural gas consumption as a function of the options installed to manage the variable RES. The obtained results allow validation of the developed approach, which shows to be flexible and easily generalizable enough to be applied to any couple of hourly and annual-resolution models and/or country. © 2022 Elsevier Ltd

关键词： Renewable energy resources

Infrastructure Model of Intelligent Pasture

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Infrastructure Model of Intelligent Pasture

2021 International Conference Automatics and Informatics, ICAI 2021

作者： Valchev, Evgeni Todorov, Jordan Stoyanov, Ivan Monov, Vladimir Dimitrov, Borislav Doukovska, Lyubka Plovdiv University 'Paisii Hilendarski' Faculty of Mathematics and Informatics Plovdiv Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Intelligent System Department Sofia Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Modeling and Optimization Department Sofia Bulgaria Consortium of New Generation Software Solution Ltd. and SmartPrint Ltd. Sofia Bulgaria

ISBN: (纸本)9781665426619

The paper presents a model of intelligent pasture incorporating three main components: one static sensor network with group of sensors located on the pasture, one dynamic sensor network with sensors indicating the movement of the herd and a communication environment necessary for data transmission and management. Communications in the model are wireless using the networking architecture LoRaWan to transmit data from the battery operated sensors. All data are stored and processed in a regional data center 'Intelligent Agriculture' with possibilities for connection with cloud structures. Intelligent tools such as machine learning and pattern recognition will be used to determine condition and quality of the pasture as well as the health of animals. © 2021 IEEE.

关键词： information management

DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution