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arXiv 2019年

作者： Gou, Chao Shen, Tianyu Zheng, Wenbo Xue, Huadan Yu, Hui Ji, Qiang Jin, Zhengyu Wang, Fei-Yue School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Radiology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China School of Creative Technologies University of Portsmouth PortsmouthPO1 2DJ United Kingdom Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Qingdao Academy of Intelligent Industries Qingdao266000 China

Data-driven artificial intelligence technologies have made much progress in medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the closed-loop optimization based on parallel execution, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decisions. Furthermore, we illustrate the preliminary implementation of PMI method through the case studies of mammogram analysis and skin lesion image analysis. Experimental results on several public medical image datasets demonstrate the effectiveness of proposed PMI. © 2019, CC BY.

关键词： Medical imaging

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I/Q Imbalances in QAM Communication systems with Multi-Antenna Receivers and IF Architecture

I/Q Imbalances in QAM Communication Systems with Multi-Anten...

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IEEE Southeastcon

作者： Thomas Yang Douglas Smith Daniel Benincasa Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach Florida Information Exploitation & Operations Division Air Force Research Laboratory Information Directorate NY

ISBN: (纸本)9781538661345

The Inphase/Quadrature phase (I/Q) imbalance is an inevitable problem in wireless communication systems due to imperfections in the transceiver hardware. In this paper, we investigate the effect of I/Q imbalance in Quadrature Amplitude Modulation (QAM) communication systems adopting multiantenna receivers and the IF (Intermediate Frequency) architecture. Two application scenarios are studied: adaptive beamforming and Blind Source Separation (BSS)-based multiuser detection. For both applications, after analyzing the interference introduced by the transceiver I/Q imbalance, we illustrate that, like systems adopting the zero-IF architecture, straightforward digital signal processing algorithms can be applied in systems with the IF architecture as well to correct the effect of I/Q imbalance. The differences between systems adopting the zero-IF and IF architectures are clarified. computer simulation results are presented to confirm the conclusions of theoretic analysis.

关键词： computer architecture Array signal processing Baseband Adaptation models Quadrature amplitude modulation Receiving antennas

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A system for work efficiency improvement by user activity analysis and feedback

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IOP Conference Series: Materials Science and engineering 2020年第1期940卷

作者： Dmitrii Timofeev Alexander Samochadin Mobile Systems Management Laboratory Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Polytekhnicheskaya ul. 29 St.Petersburg Russia Higher School of Software Engineering Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Polytekhnicheskaya ul. 29 St.Petersburg Russia

The efficiency of work processes is an important factor in company success and economic growth. Many companies are still using inefficient processes due to a lack of knowledge or defective implementation. The processes that rely upon intellectual labor, such as software development or management activities, are often undocumented, which makes them difficult for optimization. We present a method of tracking of the psychophysical state and activities of employees and providing them feedback intended to optimize the work process for work efficiency and user well-being. We use recorded traces of the user actions, including the created artifacts, and detect the psychophysical states on the base of data acquired from portable devices, including fitness wrist bands and EEG sensors. The method has been implemented in a hardware and software system whose architecture we describe in this paper. The system has been developed for the software engineering industry, but the method itself applies to other domains as well. We show how to adapt the proposed approach to different types of working environments and business processes. As the efficiency of the work process is a major factor contributing to the industrial and economic modernization, we discuss the ways to apply the method to business processes that are important for the Arctic region of Russia.

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10 quick tips for making your software outlive your job

arXiv

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arXiv 2025年

作者： Littauer, Richard Wilson, Greg Ainali, Jan AlOmar, Eman Abdullah Arabas, Sylwester Saibene, Yanina Bellini Bubendorfer, Kris Champion, Kaylea Dillon, Clare Helske, Jouni Huybrechts, Pieter Katz, Daniel S. Liao, Chang Lippert, David Liu, Fang Marshall, Pierre McCloy, Daniel R. McInerney, Ian Mkaouer, Mohamed Wiem Ojha, Priyanka Treude, Christoph White, Ethan P. CURIOSS SustainOSS GNOME Foundation Te Herenga Waka Victoria University of Wellington Te Whanganui-a-Tara Aotearoa Wellington New Zealand Third Bit TorontoON Canada Open By Default Italy Department of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States AGH University of Krakow Poland rOpenSci LP Santa Rosa Argentina Te Herenga Waka Victoria University of Wellington Wellington New Zealand Computing and Software Systems University of Washington Bothell BothellWA United States CURIOSS Lero University of Galway Ireland INVEST Research Flagship Centre University of Turku Finland Brussels Belgium University of Illinois Urbana-ChampaignIL United States Pacific Northwest National Laboratory RichlandWA United States Open Source Program Office George Washington University WashingtonDC United States Georgia Institute of Technology GA United States Oxford University United Kingdom Institute for Learning & Brain Sciences University of Washington SeattleWA United States Department of Mechanical Engineering Imperial College London London United Kingdom Department of Computer Science University of Michigan-Flint FlintMI United States Singapore Management University Singapore Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL United States

Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job—whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes. © 2025, CC BY-SA.

关键词： Human engineering

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer Dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & computer Science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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Multi-classification of Parkinson's Disease via Sparse Low-Rank Learning

Multi-classification of Parkinson's Disease via Sparse Low-R...

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International Conference on Pattern Recognition

作者： Haijun Lei Yujia Zhao Zhongwei Huang Feng Zhou Limin Huang Baiying Lei College of Computer Science & Software Engineering Guangdong Province Key Laboratory of Popular High-Performance Computers Shenzhen China Department of Industrial and Manufacturing Systems Engineering University of Michigan Dearborn Michigan USA The Operations Center of Shenzhen People's Hospital Shenzhen China School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen China

Neuroimaging techniques have been widely applied to various neurodegenerative disease analysis to reveal the intricate brain structure. The high dimensional neuroimaging features and limited sample size are the main challenges for the diagnosis task due to the unbalanced input data. To handle it, a sparse low-rank learning framework is proposed, which unveils the underlying relationships between input data and output targets by building a matrix-regularized feature network. Then we obtain the feature weight from the network based on local clustering coefficients. By discarding the irrelevant features and preserving the discriminative structured features, our proposed method can select the most relevant features and identify different stages of Parkinson's disease (PD) from normal controls. Extensive experimental results evaluated on the Parkinson's progression markers initiative (PPMI) dataset demonstrate that the proposed method achieves promising classification performance and outperforms the conventional algorithms. Furthermore, it can detect potential brain regions related to PD for future medical analysis.

关键词： Sparse matrices Feature extraction Diseases Neuroimaging Diffusion tensor imaging Support vector machines

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Causes of psychological reactance in human-computer interaction: A literature review and survey 17

Causes of psychological reactance in human-computer interact...

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35th Annual Conference of the European Association of Cognitive Ergonomics, ECCE 2017

作者： Ehrenbrink, Patrick Prezenski, Sabine Institute of Software Engineering and Theoretical Computer Science Quality and Usability Lab Technische Universitt Berlin Ernst-Reuter-Platz 7 Berlin10587 Germany Mechanical Engineering and Transport Systems Cognitive Modeling in Dynamic Human-Machine Systems Technische Universitt Berlin Marchstr. 23 Berlin10587 Germany

ISBN: (纸本)9781450352567

Psychological reactance is a psychological effect that can reduce the acceptance of devices and services. So far, there has not been a collection of potential causes for reactance in the context of human-computer interaction in literature. A literature review and a qualitative study to compile a list of possible causes of reactance in human-computer interaction were conducted. The final list includes twelve causes of reactance that have been shown to appear in the field of human-computer interaction. The individual causes are discussed and examples provided. Finally, strategies for avoiding reactance in human-computer interaction are proposed and possible future directions for reactance research in this field are recommended.

关键词： Human computer interaction

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A Second-Order Variational Framework for Joint Depth Map Estimation and Image Dehazing

A Second-Order Variational Framework for Joint Depth Map Est...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ryan Wen Liu Shengwu Xiong Huisi Wu Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China School of Computer Science and Technology Wuhan University of Technology Wuhan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781538646595

Outdoor images captured in poor weather conditions (e.g., fog or haze) commonly suffer from reduced contrast and visibility. Increasing attention has recently been paid to single image dehazing, i.e., improving image contrast and visibility. It is generally thought that the dehazing performance highly depends on the accurate depth information. In this work, we first obtain the initial depth map by using the popular dark channel prior. A unified second-order variational framework is then proposed to refine the depth map and restore the haze-free image. The introduced second-order framework has the capacity of preserving important structures in both depth map and haze-free image. Furthermore, the proposed framework performs well for several different types of haze situations. The resulting optimization problems related to depth map estimation and latent image restoration can be effectively handled using the primal-dual algorithm under a two-step numerical framework. The effectiveness of our proposed method has been demonstrated by comparing the imaging performance with several state-of-the-art dehazing methods.

关键词： Optimization TV Estimation Image restoration Imaging Navigation computer science

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Reverse Furthest Neighbors Query in Road Networks

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Journal of computer Science & Technology 2017年第1期32卷 155-167页

作者： Xiao-Jun Xu Jin-Song Bao Bin Yao Jing-Yu Zhou Fei-Long Tang Min-Yi Guo Jian-Qiu Xu School of Software Beijing Institute of Technology Beijing 100081 China First Research Institute of Ministry of Public Security Beijing 100048 China College of Mechanical Engineering Donghua University Shanghai 200051 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

Given a road network G = （V, E）, where V（E） denotes the set of vertices（edges） in G, a set of points of interest P and a query point q residing in G, the reverse furthest neighbors （RFNR） query in road networks fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in P ∪ {q}. This is the monochromatic RFNR （MRFNR） query. Another interesting version of RFNR query is the bichromatic reverse furthest neighbor （BRFNR） query. Given two sets of points P and Q, and a query point q ∈ Q, a BRFNR query fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in Q. This paper presents efficient algorithms for both MRFNR and BRFNR queries, which utilize landmarks and partitioning-based techniques. Experiments on real datasets confirm the efficiency and scalability of proposed algorithms.

关键词： reverse furthest neighbor road network landmark hierarchical partition

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