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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Gómez-López, Juan Carlos Escobar, Juan José González, Jesús Gil-Montoya, Francisco Ortega, Julio Burmester, Mike Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Granada Spain Department of Engineering University of Almería Almería Spain Department of Computer Science Florida State University Tallahassee United States

ISBN: (纸本)9783030881627

The Electroencephalography discipline studies a type of signals called Electroencephalograms (EEGs), which represent the electrical activity of different parts of the brain. EEGs are composed of a massive number of features that could be used to create an intelligent recognition system. Nevertheless, the high number of available features difficult the correct classification of the signals as most of them do not contain relevant information. The use of Feature Selection (FS) techniques allows the reduction of the number of features by extracting a reduced but powerful feature subset. Afterwards, the problem can be addressed by using both the reduced features along with classification methods. In this sense, this work proposes a comparison of five supervised classifiers to solve an Electroencephalography problem in the context of Motor Imagery-based BCI tasks. The predictive models are evaluated in terms of classification rate and performance (execution time and energy consumption), with the idea of determining which alternative offers the best trade-off among all the objectives. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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StarTroper, a film trope rating optimizer using machine learning and evolutionary algorithms

StarTroper, a film trope rating optimizer using machine lear...

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作者： García-Ortega, Rubén Héctor García-Sánchez, Pablo Merelo-Guervós, Juan Julián Development Department Badger Maps Granada Spain Department of Computer Science and Engineering University of Cádiz Cádiz Spain Department of Computer Architecture and Technology University of Granada Granada Spain

Designing a story is widely considered a crafty yet critical task that requires deep specific human knowledge in order to reach a minimum quality and originality. This includes designing at a high level different elements of the film;these high-level elements are called tropes when they become patterns. The present paper proposes and evaluates a methodology to automatically synthesize sets of tropes in a way that they maximize the potential rating of a film that conforms to them. We use machine learning to create a surrogate model that maps film ratings from tropes, trained with the data extracted and processed from huge film databases in Internet, and then we use a genetic algorithm that uses that surrogate model as evaluator to optimize the combination of tropes in a film. In order to evaluate the methodology, we analyse the nature of the tropes and their distributions in existing films, the performance of the models and the quality of the sets of tropes synthesized. The results of this proof of concept show that the methodology works and is able to build sets of tropes that maximize the rating and that these sets are genuine. The work has revealed that the methodology and tools developed are directly suitable for assisting in the plots generation as an authoring tool and, ultimately, for supporting the automatic generation of stories, for example, in massively populated videogames. © 2020 John Wiley & Sons, Ltd

关键词： Genetic algorithms

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Deceptive answer prediction with user preference graph

Deceptive answer prediction with user preference graph

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Li, Fangtao Gao, Yang Zhou, Shuchang Si, Xiance Dai, Decheng Google Research Mountain View Institute of Computing Technology CAS China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781937284503

In Community question answering (QA) sites, malicious users may provide deceptive answers to promote their products or services. It is important to identify and filter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning methods. Two kinds of features, including textual and contextual features, are investigated for this task. We further propose to exploit the user relationships to identify the deceptive answers, based on the hypothesis that similar users will have similar behaviors to post deceptive or authentic answers. To measure the user similarity, we propose a new user preference graph based on the answer preference expressed by users, such as "helpful" voting and "best answer" selection. The user preference graph is incorporated into traditional supervised learning framework with the graph regularization technique. The experiment results demonstrate that the user preference graph can indeed help improve the performance of deceptive answer prediction. © 2013 Association for Computational Linguistics.

关键词： Supervised learning

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A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques 15

A Survey on Mapping of Urban Green Spaces within Remote Sens...

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15th International Conference on computer and Automation Engineering, ICCAE 2023

作者： Burrewar, Smita Sunil Haque, Mazharul Haider, Tanwir Uddin National Institute of Technology Patna Architecture & Planning Department Bihar Patna India National Institute of Technology Patna Computer Science & Engineering Department Bihar Patna India

ISBN: (纸本)9798350396225

For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Development Goal 11.7 could come from urban green space mapping. The standard method of mapping urban green spaces requires field measurements and takes a lot of time. It is also important to update urban green space maps periodically because urban green spaces can change quickly over time due to development. With the advent of high-resolution satellite sensors like Sentinel-1 and Sentinel-2, a large number of remote sensing images may be gathered, providing quick and precise information over urban areas. This work intends to offer a new perspective on how crowd sourced geospatial big data and remote sensing may be combined to enhance the mapping of urban green spaces, including time optimization and accurate information through machine learning and deep learning. For the revitalization of cities, this data will be valuable. Remote sensing imagery data can be classified using machine learning techniques like Support vector machines (SVM), Random forests (RF), and Naive Bayes (NB). In deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), K Nearest Neighbor (KNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN) can be used to classify remote sensing images. © 2023 IEEE.

关键词： Remote sensing

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An adaptive CBR model of call center systems

An adaptive CBR model of call center systems

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8th International Conference on Control, Automation, Robotics and Vision (ICARCV 2004)

作者： Liu, HW Li, L Xuan, YL Jiang, Y Yang, Q Guang Dong University of Technology 510090 China Qing Dao Institute of Architecture and Engineering 206520 China Computer Science Department Hong Kong University of Technology Hong Kong

ISBN: (纸本)0780386531

Adaptation is one of the necessary capabilities of any expert system. In a traditional expert system, the evolving environment is often treated in a static view. And the system accepts the change negatively. Our focus in this paper is to construct an adaptive CBR model which can learn continually through detecting feedbacks from the outside to partially release this. Knowledge base here is improved gradually so to enhance the system's adaptation of solving problems in dynamic environment.

关键词： introspective learning case maintenance and indexing dynamic weights updating

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Investigating human-robot cooperation in a hospital environment: Scrutinising visions and actual realisation of mobile robots in service work 21

Investigating human-robot cooperation in a hospital environm...

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2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere, DIS 2021

作者： Tornbjerg, Kristina Kanstrup, Anne Marie Skov, Mikael B. Rehm, Matthias Department of Planning Aalborg University Denmark Department of Computer Science Aalborg University Denmark Department of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9781450384766

This study analysed work activity in a hospital basement where humans and robots interacted and cooperated on logistics tasks. The robots were deployed to automate parts of courier processes and improve the work environment for the hospital's kitchen staff. Human-robot cooperation was studied through ethnographic fieldwork relating to mobile service robots and hospital kitchen staff. The results highlighted problems arising through the assumption that the gplug and play' service robots could effectively automate work tasks. The analysis revealed the complexity of human-robot interaction in dynamic work settings such as hospitals and identified contradictions between the envisioning and realisation of robots at work, as well as the visible and invisible procedures underpinning human-robot cooperation. Consequently, we emphasise the importance of considering robots as agents of change and draw attention to the new work practices that arise when robots assume the roles of workers in dynamic work settings. © 2021 Owner/Author.

关键词： Hospitals

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Power signature analysis

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IEEE Power and Energy Magazine 2003年第2期1卷 56-63页

作者： Laughman, Christopher Lee, Kwangduk Cox, Robert Shaw, Steven Leeb, Steven Norford, Les Armstrong, Peter Lab. of Electromagnet. and Electron. Dept. of Elec. and Computer Eng. Montana State University Bozeman MT United States Department of Architecture MIT Department of Electrical Engineering Department of Mechanical Engineering Department of Building Technology

Field tests demonstrated the value of enhancements that enable an advanced nonintrusive load monitoring system to tackle complex monitoring environments. Nonintrusive load monitoring (NILM) can determine the operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. In contrast to other systems, NILM reduces sensor costs by using relatively few sensors.

关键词： Electric load forecasting

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InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

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Biomedical Signal Processing and Control 2025年 110卷

作者： Ishak Pacal Omneya Attallah Department of Computer Engineering Faculty of Engineering Igdir University 76000 Igdir Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University Nakhchivan AZ 7012 Azerbaijan Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria 21937 Egypt Wearables Biosensing and Biosignal Processing Laboratory Arab Academy for Science Technology and Maritime Transport Alexandria 21937 Egypt

Manual breast cancer diagnosis is time-consuming, error-prone, and requires skilled professionals, while deep learning-based computer-Aided Diagnosis (CAD) systems offer greater performance. Yet existing CAD systems often focus on single imaging modalities, leading to reduced accuracy when applied to different image types. Furthermore, the challenges faced by current deep learning approaches in balancing local feature extraction and global context understanding hinder the development of comprehensive diagnostic tools. To address these issues, we introduce the InceptionNext-Transformer, a novel hybrid deep learning architecture designed for multi-modal breast cancer image analysis. This innovative approach combines Convolutional Neural Networks and Vision Transformers in a unique four-stage design: two initial stages with InceptionNext blocks for multi-scale feature extraction, a third stage employing self-attention to capture global dependencies, and a final stage integrating local and global data for enhanced classification. The study presents a computationally efficient CAD for breast cancer detection applicable to resource-constrained clinical environments. The model exhibited outstanding performance when assessed on seven datasets encompassing histopathology, mammography, and ultrasound imaging modalities. In binary classification using the BreakHis histopathology dataset, it attained 100% accuracy. Using the same dataset, the method achieved 98.25% accuracy for multi-class classification. In the analysis of mammography utilizing the INbreast, MIAS, and DDSM datasets, it achieved an accuracy of 99.97%. On BUSI and BLUID ultrasound datasets, the model attained an accuracy of 92.86%. These findings validate the InceptionNeXt-Transformer’s enhanced capacity for generalization across various imaging modalities. It demonstrates superior diagnostic reliability and computational efficiency, rendering it suitable for resource-limited clinical environments.

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A Biometric Entrance Guard Control System for Improving the Entrance Security of Intelligent Rental Housing 23rd

A Biometric Entrance Guard Control System for Improving the ...

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23rd International computer Symposium, ICS 2018

作者： Lin, Jerry Chao-Lee Lin, Jim-Min Chen, Vivien Yi-Chun Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Department of Architecture Wuchang University of Technology Wuhan China Department of Architecture Fujian University of Technology No. 33 Xueyuan Road Minhou County FuzhouFujian China

ISBN: (纸本)9789811391897

With the rapid development of the information and communication technology, the security requirement of the entrance guard control systems has significantly increased. In recent years, the rise and popularity of various portable smart devices, including traditional keys, inductive buckles, inductive magnetic disks, biometrics, and so on. It has been already ripe that the biometrics, especially the finger-print identification technology from intelligence mobile phones, intelligence watches, etc., has matured and become an important member of an entrance guard control system. This research is to enhance the entrance guard control management in the traditional rental housing industry with a new generation of intelligent ultrasonic bio-metric entrance guard control system. In addition to preventing loss of cards and copy of keys, the research can also have the feature of achieving remotely monitor of the rental situation of a rented house. Even if it is not in the local area, the security of the rental house can be immediately controlled by the system to achieve the optimization result of the unattended intelligent lease access control system. Finally, the of goal of this study is to provide a convenient and safe living environment for rental houses. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Biometrics

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A Non-Stop Double Buffering Mechanism for Dataflow architecture

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Journal of computer Science & technology 2018年第1期33卷 145-157页

作者： Xu Tan Xiao-Wei Shen Xiao-Chun Ye Da Wang Dong-Rui Fan Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.

关键词： non-stop double buffering dataflow architecture high-performance computing

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