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8th International Conference Advanced computer Information Technologies, ACIT 2018

作者： Mykoliuk, Iryna Jancarczyk, Daniel Karpinski, Mikolaj Kifer, Viktor Department of Computer Science and Automatics University of Bielsko-Biala 2 Willowa St Bielsko-Biala Poland Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University 56 Ruska St Ternopil Ukraine

This document reports results that tend to confirm the applicability of the machine learning combined with signal processing for automatic atrial fibrillation detection from a short single lead electrocardiography rec... 详细信息

ISBN: (纸本)9789666544899

关键词： Electrocardiograms

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Energy Efficient Decentralized Authentication in Internet of Underwater Things Using Blockchain

Energy Efficient Decentralized Authentication in Internet of...

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IEEE Globecom Workshops

作者： Abbas Yazdinejad Reza M. Parizi Gautam Srivastava Ali Dehghantanha Kim-Kwang Raymond Choo Cyber Science Lab School of Computer Science University of Guelph Ontario Canada College of Computing and Software Engineering Kennesaw State University GA USA Department of Mathematics and Computer Science Brandon University Manitoba Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In recent years, there has been rapid growth in developing smart cities. Nearly 70% of the Earthâ€™s surface is covered by water and a large proportion of underwater environments are still unknown and have not been explored. In this context, Internet of things (IoT) is one of the most important technologies used in smart cities. Due to the growth of IoT and its influence in all areas of human life, including the underwater environment, a new class of IoT, called Internet of underwater things (IoUT) has emerged. IoUT includes a network of underwater smart devices that are connected to each other and has applications in environmental monitoring, underwater exploration, disaster prevention and military. In autonomous interactions of underwater devices, objects must be authenticated and securely interconnected to avoid security risks by malicious nodes. Most authentication methods and security mechanisms are centralized and often require a trustful third party in communications, which may well increase the computation cost and energy consumption due to the subsequent overhead, especially for underwater communications. On the other hand, there are restrictions on devices in the underwater environment, the most important of which are energy constraints. In this paper, we propose a robust, transparent, and energy-efficient decentralized authentication mechanism for IoUT using blockchain technology. We show through results that the proposed method is suitable for underwater devices with limited memory, energy, and computational power. The proposed modelâ€™s decentralized authentication in a cluster network has a significant effect on reducing the energy consumption of the devices by 74.63% compared to classic authentication methods. Moreover, using the proposed method allows a savings of more than 41.9% in end-to-end delay and increases delivery rate by 21.6%.

关键词： Authentication Internet of Things Smart cities Energy consumption Cryptography

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Business Model Canvas Should Pay More Attention to the software Startup Team

Business Model Canvas Should Pay More Attention to the Softw...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Kai-Kristian Kemell Atte Elonen Mari Suoranta Anh Nguyen-Duc Juan Garbajosa Rafael Chanin Jorge Melegati Usman Rafiq Abdullah Aldaeej Nana Assyne Afonso Sales Sami Hyrynsalmi Juhani Risku Henry Edison Pekka Abrahamsson Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Business and Economics University of Jyväskylä Jyvaskyla Finland Department of Business and IT University of Southeast Norway Bø Norway Universidad Politécnica de Madrid Madrid Spain School of Technology PUCRS Porto Alegre Brazil Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Systems University of Maryland Baltimore Country Baltimore United States Department of Software Engineering LUT University Lahti Finland Lero NUI Galway Galway Ireland

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team- related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups.

关键词： Business software Interviews Tools Task analysis software engineering Ontologies

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Where is your field going? A machine learning approach to study the relative motion of the domains of physics

arXiv

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arXiv 2019年

作者： Palmucci, Andrea Liao, Hao Napoletano, Andrea Zaccaria, Andrea Department of Physics "Sapienza" University of Rome Italy Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen China Institute for Complex Systems UOS Sapienza Rome Italy

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multidimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields. Copyright © 2019, The Authors. All rights reserved.

关键词： Vector spaces

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Fuzzy Adaptive Teaching Learning-based Optimization Strategy for GUI Functional Test Cases Generation

Fuzzy Adaptive Teaching Learning-based Optimization Strategy...

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2018年第七届软件与计算机应用国际会议

作者： Fakhrud Din Kamal Z.Zamli Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Department of Computer Science & IT University of Malakand KPK

Graphical User Interface(GUI) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with GUIs. Unlike command based interaction, GUI uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. GUI testing is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, GUI functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive Teaching Learning-based Optimization(ATLBO) algorithm, a variant of the basic Teaching Learning-based Optimization(TLBO) algorithm, for GUI functional testing is proposed. ATLBO utilizes Event-Interaction Graph(EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.

关键词： GUI Functional Testing Teaching Learning-based Optimization Mamdani Fuzzy Inference System Model-based testing

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Research on the automatic error correction model combined with artificial intelligence for college english essays

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2018年 613卷 41-51页

作者： Zhao, Dan Sun, Jian College of Foreign Languages Agricultural University of Hebei BaodingHebei China Department of Computer Application Engineering Hebei Software Institute BaodingHebei China

ISBN: (纸本)9783319607436

With the explosive development of artificial intelligence technology, the automatic error correction model combined with artificial intelligence has become a research focus in the field of artificial intelligence. Based on this, this paper proposes an automatic error correction model combined with artificial intelligence for college English essays to aim at the tedious, error-prone and time-consuming correction of college essays. Through a survey and analysis of the situation of College Students’ English essay error, this paper, taking essays of CET-6 as the research object, firstly assumes that the English essay is in a confusing system. And then an in-depth discussion is conducted on the mathematical, statistical model and technical scheme involved in the intelligent error-correction by using frequency statistics to construct a model according to the characteristics of the confusing system. Finally, correcting suggestions are given through the experiment, which realizes the intelligent error correction of words and grammatical mistakes in college English essays. The experimental results show that the proposed automatic error correction model can not only analyze the spelling errors and grammatical errors, but also correct them automatically. © 2018, Springer International Publishing AG.

关键词： Error correction

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Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects

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IEEE Reviews in Biomedical engineering 2018年 11卷 21-35页

作者： Siddiqui, Sarah Ali Zhang, Yuan Lloret, Jaime Song, Houbing Obradovic, Zoran Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Integrated Management Coastal Research Institute Universidad Politecnica de Valencia Valencia46022 Spain Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Department of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States

Keeping track of blood glucose levels non-invasively is now possible due to diverse breakthroughs in wearable sensors technology coupled with advanced biomedical signal processing. However, each user might have different requirements and priorities when it comes to selecting a self-monitoring solution. After extensive research and careful selection, we have presented a comprehensive survey on noninvasive/pain-free blood glucose monitoring methods from the recent five years (2012-2016). Several techniques, from bioinformatics, computer science, chemical engineering, microwave technology, etc., are discussed in order to cover a wide variety of solutions available for different scales and preferences. We categorize the noninvasive techniques into nonsample- and sample-based techniques, which we further grouped into optical, nonoptical, intermittent, and continuous. The devices manufactured or being manufactured for noninvasive monitoring are also compared in this paper. These techniques are then analyzed based on certain constraints, which include time efficiency, comfort, cost, portability, power consumption, etc., a user might experience. Recalibration, time, and power efficiency are the biggest challenges that require further research in order to satisfy a large number of users. In order to solve these challenges, artificial intelligence (AI) has been employed by many researchers. AI-based estimation and decision models hold the future of noninvasive glucose monitoring in terms of accuracy, cost effectiveness, portability, efficiency, etc. The significance of this paper is twofold: first, to bridge the gap between IT and medical field;and second, to bridge the gap between end users and the solutions (hardware and software). © 2008-2011 IEEE.

关键词： Cost effectiveness

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Numerical Evaluation of the Traffic Flow Indicators Using Super-Resolution Satellite Imagery

Numerical Evaluation of the Traffic Flow Indicators Using Su...

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International Multi-Conference on Industrial engineering and Modern Technologies (FarEastCon)

作者： I.N. Pugachev G.Y. Markelov V.S. Tormozov A.O. Nosenko Vice-Rector Doctor of Technical Sciences Pacific National University PNU Khabarovsk Russian Federation Computer Engineering Department Pacific National University PNU Khabarovsk Russian Federation Department of Computer Software and Automated Systems Pacific National University PNU Khabarovsk Russian Federation Foreign Language Department Pacific National University PNU Khabarovsk Russian Federation

The article is devoted to methods of numerical evaluation of traffic flow indicators (TFP) using super-resolution satellite imagery. The method includes several main stages: the stage of extracting images of the space intervals, the stage of detection and classification of vehicles, and the stage of numerical calculation of the intensity and combination of the traffic flow based on the vehicles distributing on the space interval. The main tasks and problems associated with the detection and classification of vehicles are given.

关键词： Satellites Roads Training Convolutional neural networks Urban areas Classification algorithms

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UAV-assisted Data Dissemination With Proactive Caching and File Sharing in V2X Networks

UAV-assisted Data Dissemination With Proactive Caching and F...

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IEEE Global Communications Conference

作者： Rui Lu Rongqing Zhang Xiang Cheng Liuqing Yang Department of Electronics State Key Laboratory of Advanced Optical Communication Systems and Networks School of EECS Peking University Beijing China Shenzhen Research Institute of Big Data (SRIBD) Shenzhen China School of Software Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicle-to-Everything (V2X) communications refers to an intelligent and connected vehicular network where all vehicles and infrastructure systems are interconnected with each other. Data dissemination is playing an increasingly significant role in enhancing the network connectivity and data transmission performance. However, conventional scenarios and protocols cannot satisfy the growing pluralistic and superior quality of services (QoS) requirements of included vehicles. Therefore, in this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted data dissemination protocol with proactive caching at the vehicles and an advanced file sharing strategy for revolutionizing communications. Specifically, in the proactive caching phase, we employ UAVs to act as flying base stations (BSs) for information interactions. Considering the time-variant network topology, we further propose a spatial scheduling (SS) algorithm for the trajectory optimization of each UAV, which can expedite the caching process and boost the system throughput. Then in the file sharing phase, based on the previous caching status, we provide a relay ordering algorithm to enhance the network transmission performance. Numerical results verify that our proposed UAV-assisted data transmission protocol can achieve a desirable system performance in terms of the downloading process, network throughput, and average data delivery delay.

关键词： Data dissemination Relays Protocols Quality of service Scheduling Throughput Wireless communication

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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