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International Joint Conference on Neural Networks

作者： Zexun Zhou Zhongshi He Ziyu Chen Yuanyuan Jia Haiyan Wang Jinglong Du Dingding Chen Lulu Wang Jing Chen College of Computer Science Chongqing University Chongqing China College of Medical Informatics Chongqing Medical University Chongqing China Department of Art History Sichuan Fine Arts Institute Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

Because of affected by weather conditions, camera pose and range, etc. objects are usually small, blurry, occluded and diverse pose in the images, which are gathered from outdoor surveillance cameras or access control system. It is challenging and important to detect faces precisely for face recognition system in the field of public security. In this paper, we design a context modeling network named Feature Hierarchy Encoder-Decoder Network for face detection (FHEDN), which can detect small, blurry and occluded faces hierarchy by hierarchy from the end to the beginning in a single stage. The proposed network consists of encoder and decoder subnetworks. The encoder subnetwork constructs a multi-scale feature hierarchy pyramid through VGG-16 as backbone network. The decoder subnetwork models context semantic information around face and fuses it into the feature hierarchy for face detection. In addition, we analyze the influence of distribution of training set, scale of feature hierarchy and receipt field size on the detection performance in implement stage. The experiments demonstrate that our network achieves the promising performance on AFW, PASCAL FACE, WIDER FACE and FDDB benchmarks.

关键词： Face Feature extraction Face detection Training Context modeling Decoding Semantics

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Using Playful Drawing to Support Affective Expressions and Sharing in Distributed Teams

Using Playful Drawing to Support Affective Expressions and S...

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IEEE/ACM International Workshop on Emotional Awareness in software engineering (SEmotion)

作者： Mengyao Zhao Yi Wang David Redmiles Department of Informatics University of California Irvine Department of Software Engineering Rochester Institute of Technology

ISBN: (纸本)9781538627945

Socio-emotional content is vital for building trusting, productive relationships that go beyond task-oriented communication in teams. But for distributed collaborators, it is challenging to communicate emotional status because of working over a distance. We propose to use non-work-related, non-competitive, and playful drawing online to encourage nonverbal expressions of emotions and interactions. We describe our research and preliminary results from a recent study where we demonstrated that it was effective to use freeform drawing to help distributed teams to enhance individual positive emotions. We also outline future research that could shed light on design opportunities for supporting affective expressions and sharing in distributed software engineering teams.

关键词： software engineering Conferences software Buildings Teamwork Electronic mail

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Using measurement and simulation for understanding distributed development processes in the cloud 17

Using measurement and simulation for understanding distribut...

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27th International Workshop on software Measurement and 12th International Conference on software Process and Product Measurement, IWSM Mensura 2017

作者： Lunesu, Ilaria Marchesi, Michele Münch, Jürgen Kuhrmann, Marco University of Cagliari Department of Electrical and Electronic Engineering Italy University of Cagliari Department of Mathematics and Computer Science Italy Reutlingen University Business Informatics and Herman Hollerith Center Germany Clausthal University of Technology Institute for Applied Software Systems Engineering Germany

ISBN: (纸本)9781450348539

Organizations increasingly develop software in a distributed manner. The Cloud provides an environment to create and maintain software-based products and services. Currently, it is widely unknown which software processes are suited for Cloud-based development and what their effects in specific contexts are. This paper presents a process simulation to study distributed development in the Cloud. We contribute a simulation model, which helps analyzing different project parameters and their impact on projects carried out in the Cloud. The simulator helps reproducing activities, developers, issues and events in the project, and it generates statistics, e.g., on throughput, total time, and lead and cycle time. The aim of this simulation model is thus to analyze the tradeoffs regarding throughput, total time, project size, and team size. Furthermore, the modified simulation model aims to help project managers select the most suitable planning alternative. Based on observed projects in Finland and Spain, we simulated a distributed project using artificial and real data. Particularly, we studied the variables project size, team size, throughput, and total project duration. A comparison of the real project data with the results obtained from the simulation shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. By improving the understanding of distributed development processes, our simulation model thus supports project managers in their decision-making. © 2017 Association for Computing Machinery.

关键词： Throughput

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Author Correction: Immunogenicity and efficacy of fourth BNT162b2 and mRNA1273 COVID-19 vaccine doses;three months follow-up

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Nature communications 2023年第1期14卷 1593页

作者： Michal Canetti Noam Barda Mayan Gilboa Victoria Indenbaum Michal Mandelboim Tal Gonen Keren Asraf Yael Weiss-Ottolenghi Sharon Amit Ram Doolman Ella Mendelson Dror Harats Laurence S Freedman Yitshak Kreiss Yaniv Lustig Gili Regev-Yochay The Infection Prevention & Control Unit Sheba Medical Center Tel Hashomer Ramat Gan Israel. Sackler School of Medicine Tel-Aviv University Tel Aviv Israel. ARC Innovation Center Sheba Medical Center Tel Hashomer Ramat Gan Israel. Software and Information Systems Engineering Ben-Gurion University of the Negev Be'er Sheva Israel. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Central Virology Laboratory Public Health Services Ministry of Health Tel-Hashomer Ramat Gan Israel. The Dworman Automated-Mega Laboratory Sheba Medical Center Tel-Hashomer Ramat-Gan Israel. Clinical Microbiology Sheba Medical Center Tel Hashomer Ramat Gan Israel. General Management Sheba Medical Center Tel Hashomer Ramat Gan Israel. Biostatistics and Biomathematics Unit Gertner Institute of Epidemiology and Health Policy Research Sheba Medical Center Tel Hashomer Israel. Vaccine Research Center National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. The Infection Prevention & Control Unit Sheba Medical Center Tel Hashomer Ramat Gan Israel. Gili.Regev@sheba.health.gov.il. Sackler School of Medicine Tel-Aviv University Tel Aviv Israel. Gili.Regev@sheba.health.gov.il.

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Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Anomaly Detection Based on Spatio-Temporal and Sparse Featur...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Laisen Nie Huizhi Wang Shimin Gong Zhaolong Ning Mohammad S. Obaidat Kuei-Fang Hsiao School of Electronics and Information Northwestern Polytechnical University Xi'an P.R.China Qingdao Research Institute of Northwestern Polytechnical University Qingdao P.R.China School of Intelligent Systems Engineering Sun Yat-sen University China State Key Laboratory for Novel Software Technology Nanjing 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 Department of Information Management Ming Chuan University Taiwan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

关键词： Anomaly detection Feature extraction Vehicular ad hoc networks Kernel Estimation Security Convolutional neural networks

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A Domain-Specific Language for Coordinating Collaboration

A Domain-Specific Language for Coordinating Collaboration

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Christoph Mayr-Dorn Christoph Laaber Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Informatics University of Zurich Switzerland

Manually managing collaboration becomes a problem in distributed software engineering environments. Individual engineers easily loose track of who to involve and when. The result is lack of communication, alternatively communication overload, leading to errors and rework. This paper presents a Domain-Specific Language (DSL) for scripting of collaboration structures and their evolution. We demonstrate the DSL's benefits and expressiveness for setting up an iteration planning meeting in an agile development setting.

关键词： Collaboration DSL Navigation Tools Information services Electronic publishing

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Model-Based Personalized Visualization System for Monitoring Evolving Industrial Cyber-Physical System

Model-Based Personalized Visualization System for Monitoring...

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Asia-Pacific Conference on software engineering

作者： Aitziber Iglesias Tao Yue Cristobal Arellano Shaukat Ali Goiuria Sagardui Information and Communication Technologies Area IK4-Ikerlan Research Center Arrasate Spain Department of Engineering Complex Software Systems Simula Research Laboratory Oslo Norway Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Information Systems HAZI-ISI Mondragon Unibertsitatea Arrasate Spain

ISBN: (纸本)9781728119717;9781728119700

Monitoring Industrial Cyber-Physical Systems (ICPSs) is increasingly becoming essential, which requires the development of new approaches to capture data from an ICPS for visualization, automated analyses, decision making, and anomaly detection. Towards this end, first, we present requirements for enabling personalized data visualization for monitoring evolving ICPS during its operation. Such requirements were identified from our experience of designing and developing an industrial Automated Warehouse (AW). Second, the identified requirements were validated with a questionnaire-based survey by soliciting views of industry experts from the software Monitoring Systems and Data Visualizations domains. Based on the analysis of the survey results, the need of developing personalized ICPS monitoring systems was confirmed as a step forward to enable the efficient detection of anomalies, improvement in productivity, and providing a better overview of the monitored ICPSs. Third, we developed a model-based visualization system (named as PAVS) for monitoring ICPSs, which conforms to the identified requirements, and was validated it with a dataset collected from a real AW developed by our industrial partner: ULMA Handling System, Spain. In the end, we also provide a set of lessons learned when PAVS was transferred to ULMA.

关键词： Data visualization Monitoring Germanium Runtime Industries software Cyber-physical systems

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Multimodal content representation and similarity ranking of movies

arXiv

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arXiv 2017年

作者： Bougiatiotis, Konstantinos Giannakopoulos, Theodoros Software and Knowledge Engineering Lab Institute of Informatics and Telecommunications National Center of Scientific Research Demokritos Greece Computational Intelligence Lab Institute of Informatics and Telecommunications National Center of Scientific Research Demokritos Greece

In this paper we examine the existence of correlation between movie similarity and low level features from respective movie content. In particular, we demonstrate the extraction of multi-modal representation models of movies based on subtitles, audio and metadata mining. We emphasize our research in topic modeling of movies based on their subtitles. In order to demonstrate the proposed content representation approach, we have built a small dataset of 160 widely known movies. We assert movie similarities, as propagated by the singular modalities and fusion models, in the form of recommendation rankings. We showcase a novel topic model browser for movies that allows for exploration of the different aspects of similarities between movies and an information retrieval system for movie similarity based on multi-modal content. Copyright © 2017, The Authors. All rights reserved.

关键词： Statistics

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Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project

Architecture-aware design and implementation of CNN algorith...

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International Conference on Microelectronics, ICM

作者： Paolo Meloni Daniela Loi Gianfranco Deriu Andy D. Pimentel Dolly Saprat Maura Pintort Battista Biggio Oscar Ripolles David Solans Francesco Conti Luca Benini Todor Stefanov Svetlana Minakova Bernhard Moser Natalia Shepeleva Michael Masin Francesca Palumbo Nikos Fragoulis Ilias Theodorakopoulos Department of Electrical and Electronic Engineering University of Cagliari Italy Institute of Informatics University of Amsterdam The Netherlands Pluribus One Italy CA Technologies Spain Integrated Systems Laboratory ETH Zurich Switzerland Institute of Advanced Computer Science Leiden University The Netherlands Software Competence Center Hagenberg Austria IBM Research - Haifa Israel IDEA Lab University of Sassari Italy IRIDA Labs Computer Vision Systems Greece

ISBN: (纸本)9781538681688;9781538681671

The use of Deep Learning (DL) algorithms is increasingly evolving in many application domains. Despite the rapid growing of algorithm size and complexity, performing DL inference at the edge is becoming a clear trend to cope with low latency, privacy and bandwidth constraints. Nevertheless, traditional implementation on low-energy computing nodes often requires experience-based manual intervention and trial-and-error iterations to get to a functional and effective solution. This work presents a computer-aided design (CAD) support for effective implementation of DL algorithms on embedded systems, aiming at automating different design steps and reducing cost. The proposed tool flow comprises capabilities to consider architecture-and hardware-related variables at very early stages of the development process, from pre-training hyperparameter optimization and algorithm configuration to deployment, and to adequately address security, power efficiency and adaptivity requirements. This paper also presents some preliminary results obtained by the first implementation of the optimization techniques supported by the tool flow.

关键词： Tools Inference algorithms Engines Computer architecture Computational modeling Microelectronics Training

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A large annotated medical image dataset for the development and evaluation of segmentation algorithms

arXiv

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arXiv 2019年

作者： Simpson, Amber L. Antonelli, Michela Bakas, Spyridon Bilello, Michel Farahani, Keyvan Van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc Golia-Pernicka, Jennifer Heckers, Stephan H. Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Vorontsov, Eugene Maier-Hein, Lena Jorge Cardoso, M. Department of Surgery Memorial Sloan Kettering Cancer Center Centre for Medical Image Computing University College London Center for Biomedical Image Computing and Analytics University of Pennsylvania Division of Cancer Treatment and Diagnosis National Cancer Institute Department of Pathology Radboud University Medical Center Division of Biostatistics German Cancer Research Center Department of Electrical Engineering and Computer Science Vanderbilt University Department of Informatics Technische Universität München Google DeepMind Imaging Biomarkers and Computer-aided Diagnosis Lab Radiology and Imaging Sciences National Institutes of Health Clinical Center Department of Radiology Memorial Sloan Kettering Cancer Center Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center Department of Radiology Stanford University Department of Computer Science and Software Engineering École Polytechnique de Montréal Division of Computer Assisted Medical Interventions German Cancer Research Center Department of Imaging and Biomedical Engineering King's College London

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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