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Community-aware Photo Quality Evaluation by Deeply Encoding Human Perception

IEEE Transactions on Multimedia 2019年

作者： Wang, Zepeng Li, Ping Zhang, Luming Shao, Ling Department of CSIE Hefei University of Technology Hefei China. School of Computer Science and Technology Hangzhou Dianzi University Hangzhou 310018 China and also with the State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China. College of Computer Sciences Zhejiang University Hangzhou China. Abu Dhabi United Arab Emirates. He is also with the School of Computing Sciences University of East Anglia Norwich UK.

Computational photo quality evaluation is a useful technique in many tasks of computer vision and graphics, $e.g.$, photo retaregeting, 3D rendering, and fashion recommendation. Conventional photo quality models are designed by characterizing pictures from all communities ($e.g.$, “architecture” and “colorful”) indiscriminately, wherein community-specific features are not encoded explicitly. In this work, we develop a new community-aware photo quality evaluation framework. It uncovers the latent community-specific topics by a regularized latent topic model (LTM), and captures human visual quality perception by exploring multiple attributes. More specifically, given massive-scale online photos from multiple communities, a novel ranking algorithm is proposed to measure the visual/semantic attractiveness of regions inside each photo. Meanwhile, three attributes: photo quality scores, weak semantic tags, and inter-region correlations, are seamlessly and collaboratively incorporated during ranking. Subsequently, we construct gaze shifting path (GSP) for each photo by sequentially linking the top-ranking regions from each photo, and an aggregation-based deep CNN calculates the deep representation for each GSP. Based on this, an LTM is proposed to model the GSP distribution from multiple communities in the latent space. To mitigate the overfitting problem caused by communities with very few photos, a regularizer is added into our LTM. Finally, given a test photo, we obtain its deep GSP representation and its quality score is determined by the posterior probability of the regularized LTM. Comprehensive comparative studies on four image sets have shown the competitiveness of our method. Besides, eye tracking experiments demonstrated that our ranking-based GSPs are highly consistent with real human gaze movements. IEEE

关键词： Eye tracking

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An Internet Application-Driven Cache Placement Algorithm for software-Defined Information-Centric Networking

An Internet Application-Driven Cache Placement Algorithm for...

引用

2018 International computers, Signals and Systems Conference, ICOMSSC 2018

作者： Liu, Jing Hu, Ziwei Guo, Jinghong Hao, Hanyong Tsinghua University Department of Computer Science Beijing China Beijing University of Post and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing China Global Energy Interconnection Research Institute Beijing China China State Grid Corporation of China State Grid Laboratory of Electric Power Communication Network Technology Beijing China

ISBN: (纸本)9781538667514

Currently, video has becoming one of the most traffic-intense Internet applications. However, it also causes Internet transmission problems, such as congestion, high delay and bad user experience. Therefore, it is necessary to design a cache mechanism for some popular videos in advance. To address this problem, this paper designs an Internet application-driven algorithm for centralized cache placement of popular videos in software-defined information-centric networking. First, it designs a centralized control architecture and the cache controller and node working mechanism, and then this problem is formulated as an integer programming. Finally, it proposes a greedy algorithm based on a 0-1 multiple knapsack problem to solve the integer programming and sets a performance indicator to evaluate this algorithm. The result shows that the algorithm can achieve desirable performance. © 2018 IEEE.

关键词： Integer programming

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A Bootstrapping Approach to Entity Linkage on the Semantic Web

SSRN

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SSRN 2018年

作者： Hu, Wei Jia, Cunxin National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China

In the Big Data era, ever-increasing RDF data have reached a scale in billions of entities and brought challenges to the problem of entity linkage on the Semantic Web. Although millions of entities, typically denoted by URIs, have been explicitly linked with owl:sameAs, potentially coreferent ones are still numerous. Existing automatic approaches address this problem mainly from two perspectives: one is via equivalence reasoning, which infers semantically coreferent entities but probably misses many potentials;the other is by similarity computation between property-values of entities, which is not always accurate and do not scale well. In this paper, we introduce a bootstrapping approach by leveraging these two kinds of methods for entity linkage. Given an entity, our approach firstly infers a set of semantically coreferent entities. Then, it iteratively expands this entity set using discriminative property value pairs. The discriminability is learned with a statistical measure, which does not only identify important property-values in the entity set, but also takes matched properties into account. Frequent property combinations are also mined to improve linkage accuracy. We develop an online entity linkage search engine, and show its superior precision and recall by comparing with representative approaches on a large-scale and two benchmark datasets. © 2018, The Authors. All rights reserved.

关键词： Resource Description Framework (RDF)

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JDAS: a software development framework for multidatabases

JDAS: a software development framework for multidatabases

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作者： Xie, Guoqi Chen, Yuekun Liu, Yan Fan, Chunnian Li, Renfa Li, Keqin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China Nanjing University of Information Science and Technology China Department of Computer Science State University of New York New PaltzNY United States

Modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture and framework design. Most current popular frameworks are not designed for multidatabases, and many practical problems in development arise. This study designs and implements a software development framework called Java data access service (JDAS) for multidatabases using the object-oriented programming language Java. The JDAS framework solves related problems that arise when other frameworks are employed in practical software development with multidatabases by presenting and introducing design methods. JDAS consists of the modules of the database abstract, object relational mapping, connection pools management, configuration management, data access service, and inversion of control. Results and case study reveal that the JDAS framework effectively reduces development complexity and improves development efficiency of the software systems with multidatabases. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Object oriented programming

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Secure Beamforming Design for MISO SWIPT Systems: An Indirectly Optimized Approach

Secure Beamforming Design for MISO SWIPT Systems: An Indirec...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yao Yu Siyu Wang Shumei Liu Lei Guo Zhaolong Ning Shimin Gong Mohammad S. Obaidat School of Computer Science and Engineering Northeastern University Shenyang China Chongqing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Intelligent Systems Engineering Sun Yat-sen University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

关键词： Receivers Interference Signal to noise ratio Transmitters Optimization Security Communication system security

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Secure keyboards against motion based keystroke inference attack 1

引用

12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016

作者： Du, Shaoyong Gao, Yue Hua, Jingyu Zhong, Sheng State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9783319596082

ISBN: (纸本)9783319596075

Nowadays, attackers seek various covert channels to access the users’ privacy on the mobile devices. Recent research has demonstrated that the built-in motion sensors can be exploited to monitor the users’ screen taps and infer what they have typed. This paper presents several practical and convenient countermeasures against this attack in terms of the soft keyboard. We find that this attack is sensitive to the motion noise of the mobile device and the layout variation of the soft keyboard. We, thus, present two kinds of countermeasures against this attack by introducing vibration noise in sensor readings and dynamics in the keyboard layout, respectively. We implement these countermeasures on Android platform and recruit 20 volunteers to evaluate these countermeasures’ effectiveness and usability on both the smartphones and tablets. The results show that the proposed countermeasures can effectively reduce the attackers’ keystroke inference accuracy without significantly hurting the typing efficiency. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2017.

关键词： computers

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AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

arXiv

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arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： Data acquisition

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

引用

arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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JETSCAPE framework: p+p results

引用

Physical Review C 2020年第5期102卷 054906-054906页

作者： A. Kumar Y. Tachibana D. Pablos C. Sirimanna R. J. Fries A. Majumder A. Angerami S. A. Bass S. Cao Y. Chen J. Coleman L. Cunqueiro T. Dai L. Du H. Elfner D. Everett W. Fan C. Gale Y. He U. Heinz B. V. Jacak P. M. Jacobs S. Jeon K. Kauder E. Khalaj W. Ke M. Kordell, II T. Luo M. McNelis J. Mulligan C. Nattrass D. Oliinychenko L.-G. Pang C. Park J.-F. Paquet J. H. Putschke G. Roland B. Schenke L. Schwiebert C. Shen R. A. Soltz G. Vujanovic X.-N. Wang R. L. Wolpert Y. Xu Z. Yang Department of Physics and Astronomy Wayne State University Detroit Michigan 48201 USA Department of Physics and Astronomy McGill University Montréal Quebec Canada H3A 2T8 Cyclotron Institute Texas A&M University College Station Texas 77843 USA Department of Physics and Astronomy Texas A&M University College Station Texas 77843 USA Lawrence Livermore National Laboratory Livermore California 94550 USA Department of Physics Duke University Durham North Carolina 27708 USA Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Statistical Science Duke University Durham North Carolina 27708 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Department of Physics The Ohio State University Columbus Ohio 43210 USA GSI Helmholtzzentrum für Schwerionenforschung 64291 Darmstadt Germany Institute for Theoretical Physics Goethe University 60438 Frankfurt am Main Germany Frankfurt Institute for Advanced Studies 60438 Frankfurt am Main Germany Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics Central China Normal University Wuhan 430079 China Department of Physics University of California Berkeley California 94270 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94270 USA Physics Department Brookhaven National Laboratory Upton New York 11973 USA Department of Computer Science Wayne State University Detroit Michigan 48202 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

The JETSCAPE framework is a modular and versatile Monte Carlo software package for the simulation of high energy nuclear collisions. In this work we present a new tune of JETSCAPE, called PP19, and validate it by comparison to jet-based measurements in p+p collisions, including inclusive single jet cross sections, jet shape observables, fragmentation functions, charged hadron cross sections, and dijet mass cross sections. These observables in p+p collisions provide the baseline for their counterparts in nuclear collisions. Quantifying the level of agreement of JETSCAPE results with p+p data is thus necessary for meaningful applications of JETSCAPE to A+A collisions. The calculations use the JETSCAPE PP19 tune, defined in this paper, based on version 1.0 of the JETSCAPE framework. For the observables discussed in this work calculations using JETSCAPE PP19 agree with data over a wide range of collision energies at a level comparable to standard Monte Carlo codes. These results demonstrate the physics capabilities of the JETSCAPE framework and provide benchmarks for JETSCAPE users.

关键词： Fragmentation into hadrons Jets & heavy flavor physics Particle & resonance production Relativistic heavy-ion collisions

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