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An Overview of Bag of Words;Importance, Implementation, Applications, and Challenges 5

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An Overview of Bag of Words;Importance, Implementation, Appl...

5th International engineering Conference on Developments in Civil and computer engineering Applications, IEC 2019

作者： Qader, Wisam A. Ameen, Musa M. Ahmed, Bilal I. Tishk International University Computer Engineering Dep. Erbil Iraq Tishk International University Information Technology Dep. Erbil Iraq

ISBN: (纸本)9781728143774

In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot localization, etc. It is one of the most common methods for the categorization of text and objects. In text classification, the BoW method records the number of occurrences of each bag that is created for each instance type or word disregarding the order of the words or the grammar. And in visual scene classification it is based on clusters of local descriptors which are taken from the images disregarding the order of the clusters. The key idea is to generate a histogram for the words in the documents or the features in the images to represent the specified document or image. The BoW method is computationally and even conceptually is simpler than many other classification methods. For that reason, BoW based systems could record new and higher performance scores on common used benchmarks of text and image classification algorithms. This paper presents an overview of BoW, importance of BoW, how does it work, applications and challenges of using BoW. This study is useful in terms of introducing the BoW method to the new researchers and providing a good background with associated related works to the researchers that are working on the model. © 2019 IEEE.

关键词： Image classification

Modelling Value-oriented Legal Reasoning in LOGIKEY

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arXiv 2020年

作者： Benzmüller, Christoph Fuenmayor, David Lomfeld, Bertram University of Bamberg AI Systems Engineering Freie Universität Berlin Dep. of Mathematics and Computer Science Germany Université du Luxembourg Dep. of Computer Science Freie Universität Berlin Dep. of Mathematics and Computer Science Germany Freie Universität Berlin Dep. of Law Germany

The logico-pluralist LOGIKEY knowledge engineering methodology and framework is applied to the modelling of a theory of legal balancing in which legal knowledge (cases and laws) is encoded by utilising context-dep.ndent value preferences. The theory obtained is then used to formalise, automatically evaluate, and reconstruct illustrative property law cases (involving appropriation of wild animals) within the Isabelle/HOL proof assistant system, illustrating how LOGIKEY can harness interactive and automated theorem proving technology to provide a testbed for the development and formal verification of legal domain-specific languages and theories. Modelling value-oriented legal reasoning in that framework, we establish novel bridges between latest research in knowledge representation and reasoning in non-classical logics, automated theorem proving, and applications in legal *** Codes 68T01 68T27 68T30 03Axx 03B16 03B35 03B45 03B60 03B70 Copyright © 2020, The Authors. All rights reserved.

关键词： Theorem proving

CGAN-ECT: TOMOGRAPHY IMAGE RECONSTRUCTION FROM ELECTRICAL CAPACITANCE MEASUREMENTS USING CGANS

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arXiv 2022年

作者： Deabes, Wael Abdel-Hakim, Alaa E. Texas A&M University-San Antonio One University Way HALL 334 San AntonioTX78224 United States Computer Science Dep. in Jamoum Umm Al-Qura University Electrical Engineering Department Assiut University Assiut71516 Egypt

Due to the rapid growth of Electrical Capacitance Tomography (ECT) applications in several industrial fields, there is a crucial need for developing high quality, yet fast, methodologies of image reconstruction from raw capacitance measurements. Deep learning, as an effective non-linear mapping tool for complicated functions, has been going viral in many fields including electrical tomography. In this paper, we propose a Conditional Generative Adversarial Network (CGAN) model for reconstructing ECT images from capacitance measurements. The initial image of the CGAN model is constructed from the capacitance measurement. To our knowledge, this is the first time to represent the capacitance measurements in an image form. We have created a new massive ECT dataset of 320K synthetic image-measurements pairs for training, and testing the proposed model. The feasibility and generalization ability of the proposed CGAN-ECT model are evaluated using testing dataset, contaminated data and flow patterns that are not exposed to the model during the training phase. The evaluation results prove that the proposed CGAN-ECT model can efficiently create more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms. CGAN-ECT achieved an average image correlation coefficient of more than 99.3% and an average relative image error about 0.07. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

How Can Businesses, Low-Tech Businesses in Particular, Benefit from the Metaverse? A Delphi Perspective from Academics

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How Can Businesses, Low-Tech Businesses in Particular, Benef...

International Conference on Marketing and Technologies, ICMarkTech 2023

作者： Lopes, Teresa Sofia Amorim Carreira, Rui Carlos, Vera Silva Pereira, Rui Humberto Alves, Carlos Pratas, Joaquim dos Santos, José Paulo Marques Department of Business Administration University of Maia Maia Portugal NIPE—Centre for Research in Economics and Management School of Economics and Management University of Minho Braga Portugal Polytechnic Institute of Maia Maia Portugal LIACC—Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal NECE—Research Center for Business Sciences Dep. of Management and Economics University of Beira Interior Covilhã Portugal CEOS.PP—Centre for Organizational and Social Studies Polytechnic of Porto Porto Portugal Algoritmi Research Centre University of Minho Guimarães Portugal LASI—Intelligent Systems Associate Laboratory Guimarães Portugal Porto Accounting and Business School Polytechnic Institute of Porto São Mamede de Infesta Portugal

ISBN: (纸本)9789819715510

Two years ago, Meta was born, accompanied by the announcement of the Metaverse. The Metaverse would be a collection of 3D immersive ecosystems for people’s socialization. This innovative idea should allow one to live in the Metaverse as in real life without physical barriers. Attending shows, concerts, virtual travel, and caring for one’s health or education should be possible. In sum, a new marketplace is born. Amid such newness, the question of how businesses may benefit from the Metaverse arises. Albeit tech businesses must be comfortable and adapted to the potentialities of the new marketplace, the entrance of the low-techs may be more problematic. The study brings together a panel of academic experts to reflect on these questions. They agree that there is a myriad of potentialities in the new marketplace, not only for B2C but also for B2B. Low-techs may be less adapted to benefit from the Metaverse. A following-the-wave strategy must be avoided. On the contrary, investing in understanding the new paradigm and careful planning is essential. The Metaverse has no physical barriers, which tend to limit low-techs’ actions. Their nonexistence creates new opportunities and must be where customers are, after all. The Metaverse, however, still needs development to gain critical mass. Hardware easy to use, its cost, speedy and cheap Internet, and mainly, trustable and recognized currencies are still required. Nonetheless, the Metaverse may be a new world full of opportunities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Commerce

Knowledge Cores in large formal contexts

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arXiv 2020年

作者： Hanika, Tom Hirth, Johannes Knowledge and Data Engineering Group Dep. of Electrical Engineering and Computer Science University of Kassel

Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called k-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data *** Codes 68T30 03G10 97R40 Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge based systems

Robust machine learning in critical care - Software engineering and medical perspectives

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arXiv 2021年

作者： Staron, Miroslaw Hergès, Helena Odenstedt Naredi, Silvana Block, Linda El-Merhi, Ali Vithal, Richard Elam, Mikael Computer Science and Engineering Chalmers University of Gothenburg Gothenburg Sweden Dep. of Anesthesiology and Intensive Care Sahlgrenska Academy Region Västra Götaland Sahlgrenska University Hospital Dep. of Anesthesia and Intensive Care Gothenburg Sweden Institute of Neurosc. and Physiology Dep. of Clinical Neuroscience Sahlgrenska Academy Dep. of Clinical Neurophysiology Sahlgrenska Univ. Hospital MedTech West Sahlgrenska Univ. Hospital Gothenburg Sweden

Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems. Therefore, developing reliable software for monitoring critically ill patients requires close collaboration between physicians and software engineers. However, these two different disciplines need to find own research perspectives in order to contribute to both the medical and the software engineering domain. In this paper, we address the problem of how to establish a collaboration where software engineering and medicine meets to design robust machine learning systems to be used in patient care. We describe how we designed software systems for monitoring patients under carotid endarterectomy, in particular focusing on the process of knowledge building in the research team. Our results show what to consider when setting up such a collaboration, how it develops over time and what kind of systems can be constructed based on it. We conclude that the main challenge is to find a good research team, where different competences are committed to a common goal. Copyright © 2021, The Authors. All rights reserved.

关键词： Software design

Object Detection During Newborn Resuscitation Activities

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arXiv 2023年

作者： Meinich-Bache, Øyvind Engan, Kjersti Austvoll, Ivar Eftestøl, Trygve Myklebust, Helge Yarrot, Ladislaus Blacy Kidanto, Hussein Ersdal, Hege The Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Laerdal Medical Norway The Research Institute Haydom Lutheran Hospital Manyara Tanzania United Republic of The School of Medicine Aga Khan University Dar es Salaam Tanzania United Republic of The Faculty of Health Sciences University of Stavanger Norway The Department of Anesthesiology and Intensive Care Stavanger University Hospital Norway

Objective: Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations;however, the importance and effect of the different treatments are not fully explored. The available data is collected in Tanzania, during newborn resuscitation, for analysis of the resuscitation activities and the response of the newborn. An important step in the analysis is to create activity timelines of the episodes, where activities include ventilation, suction, stimulation etc. Methods: The available recordings are noisy real-world videos with large variations. We propose a two-step process in order to detect activities possibly overlapping in time. The first step is to detect and track the relevant objects, like bag-mask resuscitator, heart rate sensors etc., and the second step is to use this information to recognize the resuscitation activities. The topic of this paper is the first step, and the object detection and tracking are based on convolutional neural networks followed by post processing. Results: The performance of the object detection during activities were 96.97 % (ventilations), 100 % (attaching/removing heart rate sensor) and 75 % (suction) on a test set of 20 videos. The system also estimate the number of health care providers present with a performance of 71.16 %. Conclusion: The proposed object detection and tracking system provides promising results in noisy newborn resuscitation videos. Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities. © 2023, CC BY.

关键词： Object detection

Activity Recognition from Newborn Resuscitation Videos

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arXiv 2023年

作者： Meinich-Bache, Øyvind Austnes, Simon Lennart Engan, Kjersti Austvoll, Ivar Eftestøl, Trygve Myklebust, Helge Kusulla, Simeon Kidanto, Hussein Ersdal, Hege The Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Laerdal Medical Norway The Research Institute Haydom Lutheran Hospital Manyara Tanzania United Republic of The School of Medicine Aga Khan University Dar es Salaam Tanzania United Republic of The Faculty of Health Sciences University of Stavanger Norway The Department of Anesthesiology and Intensive Care Stavanger University Hospital Norway

Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes. Methods: We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs. Results: The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %. Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32 %. Conclusion: The results indicate that the proposed CNN-based two-step ORAA-net could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos. Significance: A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation. © 2023, CC BY.

关键词： Object detection

Extremum Seeking Control for Beam Steering using Hypersurfaces

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Extremum Seeking Control for Beam Steering using Hypersurfac...

IEEE International Conference on Communications Workshops, ICC

作者： Nouman Ashraf Marios Lestas Taqwa Saeed Hamidreza Taghvaee Sergi Abadal Andreas Pitsillides Christos Liaskos Dep. of Electrical Engineering Frederick University Dep. of Computer Science University of Cyprus Nicosia Cyprus Dep. of Computer Architecture Universitat Politècnica de Catalunya Barcelona Spain Institute of Computer Science Foundation of Research and Technology Hellas Heraklion Greece

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Hypersurface, a software defined metasurface (SDM) paradigm constitutes a revolutionary technology aiming at offering programmatic control over all aspects of a propagating wave, altering its direction, power, polarization and phase. In the absence of line of sight (LOS) between a transmitter and a receiver, HSF can provide seamless connectivity via programmatic reflection. However, such an application requires fine tuning of the metasurface reconfiguration parameters, which may not be effective when done in an open loop fashion due to model uncertainties. In this work, we consider a feedback-based formulation of the problem, which involves maximization of the received power and propose the use of Extremum Seeking Control (ESC) for the controller implementation due to fact that it is model-free and adheres to the maximization problem. Extensive simulations indicate that the proposed scheme is successful in guiding the impinging wave to the receiver within reasonable time even in the presence of time varying delays incurred by message propagation. In addition, a discrete time implementation is considered, investigating its limitations as we increase the sampling time while also characterizing the traffic within the controller network.

关键词： Receivers Beam steering Transmitters Aerospace electronics computer science Software Delays