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Fall classification based on sensor data from smartphone and smartwatch

AIP Conference Proceedings 2019年第1期2112卷

作者： Surya Suresh Mihir Jain Radha Ramadoss Department of Computer Science and Engineering SRM Institute of Science and Technology Kancheepuram Dt. Tamil Nadu Kattankulathur-603203 India Department of Computer Science and Engineering SRM Institute of Science and Technology Kancheepuram Dt. Tamil Nadu Kattankulathur-603203 India Assistant Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Kancheepuram Dt. Tamil Nadu Kattankulathur-603203 India

A fall is an unintentional event in which a person comes to rest on the ground/floor/a lower level. Falls are the second driving cause of inadvertent injury deaths around the world. Falls pose a major health risk to children and the elderly (especially above the age of 65). Injuries from falls make it difficult for an individual to move around, do regular activities, or live on their own. Hence, studies on methods able to recognize Activities of Daily Living (ADLs) and to identify falls is on the rise. The acknowledgment of ADLs may permit to gather the sum of physical activity that a subject performs daily, whilst the timely identification of falls may aid in diminishing their consequences. To improve the accuracy of the classifier, the data from smartwatches and smartphones are combined. There are not many publicly accessible datasets incorporating data from both smartwatchesand smartphones. Hence, the data will be collected independently. The Decision tree (J48) classifier will be used to classify the falls. The evaluation metrics include: Precision, Recall, F1 Score and the confusion matrix.

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Preface

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Journal of computer science & technology 2019年第3期34卷 507-508页

作者： Shi-Min Hu Hongbo Fu Marcus Magnor Department of Computer Science and Technology Tsinghua University.Beijing School of Creative Media City University of Hong KongHong Kong Computei Graphics Laboratory Braunschweig University of TechnologyBraunschweig

The Computational Visual Media (CVM) conference series is intended to provide a major international forum for exchanging novel research ideas and significant computational methods that either underpin or apply visual media. The primary goal is to promote cross-disciplinary research to amalgamate aspects of computer graphics, computer vision, machine learning, image and video processing, visualization and geometric computing. The main topics of interest to CVM include classification, composition, retrieval, synthesis, cognition and understanding of visual media (e.g., images, videos, 3D geometry).

关键词： Preface Computational Visual Media(CVM) primary goal

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Goal Programming Model for Optimizing Working Capital Management: Case of Tire Retreading Company

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Journal of Operations and Strategic Planning 2018年第2期1卷 148 - 167页

作者： Arunkumar O. N. Divya D. Tony Mathew Associate Professor Department of Mechanical Engineering Sree Narayana Gurukulam College of Engineering Kadayiruppu Kolenchery Ernakulam Kerala India. Assistant Professor Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Vidya Bharathi Nagar Mattoor Kalady Ernakulam District Kerala India. Assistant Professor Department of Mechanical Engineering Kottayam Institute of Technology and Science Kottayam Kerala India.

This article discusses goal programming model for optimizing working capital management (WCM) of a tire manufacturing company. The mathematical model is run for the years 2008–2009 to 2012–2013 and values of the respective goals are taken from it. The validation for the model values with the actual values from the financial data are carried out to check the significance of the model values. The significance test is carried out in one sample t-test. It is observed that all the values got from the model are significant. The sales of the company for the year 2013–2014 is forecasted from the past data by linear regression using Microsoft Excel as a trend line. The model is used to 2analyze the values of variables of WCM for the year 2013–2014 to achieve a 30 percent return on assets (ROA) for the forecasted sales.

关键词： Working capital management goal programming LINGO tire manufacturing firm

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of Information Management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment

SSBC 2020: Sclera Segmentation Benchmarking Competition in t...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： M. Vitek A. Das Y. Pourcenoux A. Missler C. Paumier S. Das I. De Ghosh D. R. Lucio L. A. Zanlorensi D. Menotti F. Boutros N. Damer J. H. Grebe A. Kuijper J. Hu Y. He C. Wang H. Liu Y. Wang Z. Sun D. Osorio-Roig C. Rathgeb C. Busch J. Tapia A. Valenzuela G. Zampoukis L. Tsochatzidis I. Pratikakis S. Nathan R. Suganya V. Mehta A. Dhall K. Raja G. Gupta J. N. Khiarak M. Akbari-Shahper F. Jaryani M. Asgari-Chenaghlu R. Vyas S. Dakshit P. Peer U. Pal V. Štruc University of Ljubljana (UL SI) Indian Statistical Institute (lSI IN) Grenoble Institute of Technology (INP FR) Ècole Nationale Supérieure de 1’Èlectronique et de ses Applications (ENSEA FR) University of Engineering and Management (UEM IN) Barrackpore Rastraguru Surendranath College (BRSNC IN) Federal University of Paraná (UFPR BR) Technische Universität Darmstadt (TUD DE) Fraunhofer Institute for Computer Graphics Research (IGD DE) Chinese Academy of Sciences (CAS CN) Beijing University of Civil Engineering and Architecture (BUCEA CN) Hochschule Darmstadt (HDA DE) Universidad de Santiago (USACH CL) TOC Biometrics (CL) Democritus University of Thrace (DUTH GR) Couger Inc. (JP) Thiagarajar College of Engineering (TCE IN) Indian Institute of Technology Ropar (IIT-RPR IN) Monash University (MU AU) Norwegian University of Science and Technology (NTNU NO) Warsaw University of Technology (WUT PL) Tabriz University (TU IR) Arak University (AU IR) Bennet University (BU IN) Calcutta Institute of Engineering and Management Maulana Abul Kalam University of Technology (MAKAUT IN) University of Texas at Dallas (UTD US

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.

关键词： Image segmentation Mobile handsets Performance evaluation Benchmark testing Training Lighting Iris recognition

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The zero resource speech challenge 2019: TTS without T

arXiv

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arXiv 2019年

作者： Dunbar, Ewan Algayres, Robin Karadayi, Julien Bernard, Mathieu Benjumea, Juan Cao, Xuan-Nga Miskic, Lucie Dugrain, Charlotte Ondel, Lucas Black, Alan W. Besacier, Laurent Sakti, Sakriani Dupoux, Emmanuel Laboratoire de Linguistique Formelle CNRS Paris Diderot Sorbonne Paris Cité France Cognitive Machine Learning ENS CNRS EHESS INRIA PSL Research University France Department of Computer Graphics and Multimedia Brno Univ. of Technology Czech Republic Language Technologies Institute Carnegie Mellon University United States Laboratoire d'Informatique de Grenoble GETALP Université Grenoble Alpes France Nara Institute of Science and Technology RIKEN Center for Advanced Intelligence Project Japan Facebook A.I. Research Paris France

We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels. Participants must discover subword units in an unsupervised way (using the Unit Discovery dataset) and align them to the voice recordings in a way that works best for the purpose of synthesizing novel utterances from novel speakers, similar to the target speaker's voice. We describe the metrics used for evaluation, a baseline system consisting of unsupervised subword unit discovery plus a standard TTS system, and a topline TTS using gold phoneme transcriptions. We present an overview of the 19 submitted systems from 10 teams and discuss the main results. Copyright © 2019, The Authors. All rights reserved.

关键词： Speech synthesis

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Determination of the discretion interval of the temporal series of the technological process parameter measurement in ACS TP in the noises conditions

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Journal of Physics: Conference Series 2021年第1期1781卷

作者： S O Tymchuk I G Abramenko K V Zahumenna O O Miroshnyk T G Shchur A Tomporowski W Kruszelnicka Kharkiv Petro Vasilenko National Technical University of Agriculture automation and the computer integrated technologies department Rizdviana str. 19 Kharkiv Ukraine Lviv National Agrarian University Department of operation and technical service of machines named after Professor Semkovych O.D. Volodymyr the Great str 1. 80381 Dublyany Zhovkva district Lviv region Ukraine University of Science and Technology in Bydgoszcz Faculty of Mechanical Engineering Al. Prof. S. Kaliskiego no. 7 85-796 Bydgoszcz Poland

We have solved the problem of substantiating the choice of the sampling interval of the temporal series of the technological process parameter measurements for automation systems, if there are errors, which takes into account the frequency characteristics of this series. The object of the study is the measuring channel of ACS TP (automated control systems of technological processes). We have developed the mathematical simulation model of the measuring channel and the analog-to-digital converter, which provides the possibility of studying the influence of the sampling interval value of the temporal series of technological process parameter measurements on the reliability of the information. The results of the research of the model show the effectiveness of the model developed.

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Solid model edit distance: a multi-application and multi-level schema for CAD model retrieval

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computer Aided Drafting,Design and Manufacturing 2017年第1期27卷 22-38页

作者： Wang Bin Hu Kaimo Li Dong Zhang Hui School of Software Tsinghua University Tsinghua National Laboratory for Information Science and Technology Department of Computer Graphics Technology Purdue University

We present the solid model edit distance(SMED),a powerful and flexible paradigm for exploiting shape similarities amongst CAD *** is designed to measure the magnitude of distortions between two CAD models in boundary representation(B-rep).We give the formal definition by analogy with graph edit distance,one of the most popular graph matching *** avoid the expensive computational cost potentially caused by exact computation,an approximate procedure based on the alignment of local structure sets is provided in *** order to verify the flexibility,we make intensive investigations on three typical applications in manufacturing industry,and describe how our method can be adapted to meet the various ***,a multilevel method is proposed to make further improvements of the presented algorithm on both effectiveness and efficiency,in which the models are hierarchically segmented into the configurations of *** results show that SMED serves as a reasonable measurement of shape similarity for CAD models,and the proposed approach provides remarkable performance on a real-world CAD model database.

关键词： CAD model retrieval solid model edit distance graph edit distance boundary representation

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Securing smart grid data under key exposure and revocation in cloud computing

Securing smart grid data under key exposure and revocation i...

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International Conference on Circuits, Communication, Control and Computing

作者： J M Navya H A Sanjay KM Deepika Mtech 4 sem (computer network engineering) Department of Information science engineering Nitte meenakshi institute of technology Bangalore India HOD Department of information science and engineering Nitte meenakshi institute of technology Bangalore India Asst.Professor Department of information science and engineering Nitte meenakshi institute of technology Bangalore India

ISBN: (纸本)9781538684887

Smart grid systems data has been exposed to several threats and attacks from different perspectives and have resulted in several system failures. Obtaining security of data and key exposure and enhancing system ability in data collection and transmission process are challenging, on the grounds smart grid data is sensitive and enormous sum. In this paper we introduce smart grid data security method along with advanced Cipher text policy attribute based encryption (CP-ABE). Cloud supported IoT is widely used in smart grid systems. Smart IoT devices collect data and perform status management. Data obtained from the IOT devices will be divided into blocks and encrypted data will be stored in different cloud server with different encrypted keys even when one cloud server is assaulted and encrypted key is exposed data cannot be decrypted, thereby the transmission and encryption process are done in correspondingly. We protect access-tree structure information even after the data is shared to user by solving revocation problem in which cloud will inform data owner to revoke and update encryption key after user has downloaded the data, which preserves the data privacy from unauthorized users. The analysis of the system concludes that our proposed system can meet the security requirements in smart grid systems along with cloud-Internet of things.

关键词： Encryption Smart grids Cloud computing Ciphers Servers

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