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Educational games for cultural heritage 1

1st International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, VIPERC 2019

作者： Vocaturo, Eugenio Zumpano, Ester Caroprese, Luciano Pagliuso, Saverio Mario Lappano, Divina University of Calabria DIMES Department of Computer Science Modelling Electronic and System Engineering Rende Italy University of Waterloo Canada

Educational games appear as a new tool for learning cultural content in an engaging way. The applications with augmented reality in virtual environments are able to support the experience of cultural heritage, overcoming the "tangibility" barriers of museums, exhibitions, books and audio-visual content. We intend to provide a unified view regarding the game genres used to profitably manage the contents of cultural heritage, highlighting the training objectives of the games in this sector and analyzing the complex relationships between gender, context of use, technological solutions and learning effectiveness as well as the identification of the most desirable features for the design phase. © 2018 CEUR-WS. All rights reserved.

关键词： Augmented reality

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Analysis of security issues of cloud-based web applications

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Journal of Ambient Intelligence and Humanized Computing 2020年第7期12卷 7051-7062页

作者： Mishra, Shailendra Sharma, Sunil Kumar Alowaidi, Majed A. Department of Computer Engineering College of Computer and Information Sciences Majmaah University Majmaah Saudi Arabia Department of Information System College of Computer and Information Sciences Majmaah University Majmaah Saudi Arabia Department of Information Technology College of Computer and Information Sciences Majmaah University Majmaah Saudi Arabia

The Cloud computing is a powerful tool to optimize the cost in terms of hardware, controllable, utility to sharing the data, due to abovementioned features most of the organization switching their applications and services on the cloud. Cloud services offer secure and scalable services, but there is always some security problem when data have transmitted from a central storage server to a different cloud, personal and private data commitment increase risk of data confidentiality, integrity, availability, and authentication before one choose a vendor in the cloud or choose the cloud and move services in the cloud. This paper aims to resolve issues and provide the countermeasures relating to security issues in clouds based web applications. The vulnerability scores, its impact on confidentiality, integrity, availability, access complexity, and risk on assets calculated, and it observed most of the vulnerabilities identified during the scanning are related to security due to mis-configurations of web servers. The results analyzed for the vulnerability scores, vulnerabilities impact on confidentiality, integrity, availability, access complexity, and risk are analyzed description, and risk assessment. The countermeasures for each vulnerability based on the experimental results discussed.

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Anomaly Detection on Patients Undergoing General Anesthesia 12th

Anomaly Detection on Patients Undergoing General Anesthesia

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International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information systems, CISIS 2019 and 10th International Conference on European Transnational Education, ICEUTE 2019

作者： Jove, Esteban Gonzalez-Cava, Jose M. Casteleiro-Roca, José-Luis Quintián, Héctor Méndez-Pérez, Juan Albino Calvo-Rolle, José Luis Department of Industrial Engineering University of A Coruña Avda. 19 de febrero s/n Ferrol A Coruña15405 Spain Department of Computer Science and System Engineering Universidad de La Laguna Avda. Astrof. Francisco Sánchez s/n S/C de Tenerife38200 Spain

ISBN: (纸本)9783030200046

The importance of the infusion drug optimization in patients undergoing general anesthesia has led to the implementation of automatic control loops and models to predict the state of the patient. The appearance of any anomaly during the anesthetic process may lead, for instance, to incorrect drug administration. This could produce undesirable side effects that can affect the patient postoperative and also reduce the safety of the patient in the operating room. This study evaluates different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final results give good performance in general terms. © 2020, Springer Nature Switzerland AG.

关键词： Anomaly detection

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BoostCaps: A Boosted Capsule Network for Brain Tumor Classification

BoostCaps: A Boosted Capsule Network for Brain Tumor Classif...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Parnian Afshar Konstantinos N. Plataniotis Arash Mohammadi Concordia Institute for Information System Engineering (CIISE) Concordia University Montreal QC Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Brain tumor is among the deadliest cancers, whose effective treatment is partially dependent on the accurate diagnosis of the tumor type. Convolutional neural networks (CNNs), which have been the state-of-the-art in brain tumor classification, fail to identify the spatial relations in the image. Capsule networks, proposed to overcome this drawback, are sensitive to miscellaneous backgrounds and cannot manage to focus on the main target. To address this shortcoming, we have recently proposed a capsule network-based architecture capable of taking both brain images and tumor rough boundary boxes as inputs, to have access to the surrounding tissue as well as the main target. Similar to other architectures, however, this network requires extensive search within the space of all possible configurations, to find the optimal architecture. To eliminate this need, in this study, we propose a boosted capsule network, referred to as BoostCaps, which takes advantage of the ability of boosting methods to handle weak learners, by gradually boosting the models. BoosCaps, to the best of our knowledge, is the first capsule network model that incorporates an internal boosting mechanism. Our results show that the proposed BoostCaps framework outperforms its single capsule network counterpart.

关键词： Tumors Boosting Brain Cancer computer architecture Image segmentation

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Photonic real time video image signal processor at 17Tb/s based on a Kerr microcomb

arXiv

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arXiv 2024年

作者： Tan, Mengxi Xu, Xingyuan Boes, Andreas Corcoran, Bill Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Wu, Jiayang Mitchell, Arnan Moss, David J. School of Electronic and Information Engineering Beihang University Beijing100191 China Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia School of Engineering RMIT University MelbourneVIC3001 Australia State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Electrical and Electronic Engineering University of Adelaide Adelaide5005 SA Australia Department of Electrical and Computer System Engineering Monash University ClaytonVIC3168 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi’an Institute of Optics and Precision Mechanics CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada

Signal processing has become central to many fields, from coherent optical telecommunications, where it is used to compensate signal impairments, to video image processing. Image processing is particularly important for observational astronomy, medical diagnosis, autonomous driving, big data and artificial intelligence. For these applications, signal processing traditionally has mainly been performed electronically. However these, as well as new applications, particularly those involving real time video image processing, are creating unprecedented demand for ultrahigh performance, including high bandwidth and reduced energy consumption. Here, we demonstrate a photonic signal processor operating at 17 Terabits/s and use it to process video image signals in real-time. The system processes 400,000 video signals concurrently, performing 34 functions simultaneously that are key to object edge detection, edge enhancement and motion blur. As compared with spatial-light devices used for image processing, our system is not only ultra-high speed but highly reconfigurable and programable, able to perform many different functions without any change to the physical hardware. Our approach is based on an integrated Kerr soliton crystal microcomb, and opens up new avenues for ultrafast robotic vision and machine learning. © 2024, CC BY.

关键词： Energy utilization

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Pruning random resistive memory for optimizing analogue AI

arXiv

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arXiv 2023年

作者： Li, Yi Wang, Songqi Zhao, Yaping Wang, Shaocong Zhang, Woyu He, Yangu Lin, Ning Cui, Binbin Chen, Xi Zhang, Shiming Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Xu, Xiaoxin Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong State Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences Beijing100049 China Institute of Mind The University of Hong Kong Hong Kong

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability, which are exacerbated by the increasing number of users and the development of even larger models. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility that addresses the von Neumann bottleneck, slowdown of Moore’s law, and volatile DRAM of conventional digital hardware. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Therefore, leveraging the efficiency advantage while mitigating the programming disadvantage of analogue computing with resistive memory is a major open problem in AI hardware and electronics communities. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, r

关键词： Energy utilization

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A new model to analyze power and communication system intra-and-inter dependencies

arXiv

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arXiv 2020年

作者： Roy, Sohini Chandrasekaran, Harish Pal, Anamitra Sen, Arunabha School of Computing Informatics and Decision System Engineering School of Electrical Computer and Energy Engineering Arizona State University Tempe- AZ85281 United States

—The reliable and resilient operation of the smart grid necessitates a clear understanding of the intra-and-inter dependencies of its power and communication systems. This understanding can only be achieved by accurately depicting the interactions between the different components of these two systems. This paper presents a model, called modified implicative interdependency model (MIIM), for capturing these interactions. Data obtained from a power utility in the U.S. Southwest is used to ensure the validity of the model. The performance of the model for a specific power system application namely, state estimation, is demonstrated using the IEEE 118-bus system. The results indicate that the proposed model is more accurate than its predecessor, the implicative interdependency model (IIM) [1], in predicting the system state in case of failures in the power and/or communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Phasor measurement units

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SiamSTU: Hyperspectral video tracker based on spectral spatial angle mapping enhancement and state aware template update

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Infrared Physics & Technology 2025年 150卷

作者： Dong Zhao Haorui Zhang Pattathal V. Arun Changzhe Jiao Huixin Zhou Pei Xiang Kuanhong Cheng Jiangsu Province Engineering Research Center of Photonic Devices and System Integration for Communication Sensing Convergence Wuxi University Wuxi 214105 China Computer Science and Engineering Group Indian Institute of Information Technology Sri City 441108 India School of Artificial Intelligence Xidian University Xi’an 710051 China School of Physics Xidian University Xi’an 710051 China School of Computer Science and Engineering Xi’an University of Technology Xi’an 710051 China

Recently, the research in hyperspectral video tracking has gained wide attention, and the Siamese-based approaches have reported the state-of-the-art results. However, most of the state-of-the-art approaches have several limitations, including the failure of the dimensionality reduction (DR) to enhance the target and suppress the background. Also, the target and search branches of most these approaches operate independently without exchanging information, and the target template remains unchanged during tracking. In this regard, the current paper introduces a novel model to overcome these challenges. Firstly, we apply spectral spatial angle mapping enhancement for DR, which not only reduces the dimensionality but also improves the distinction between the target and background. Secondly, an information exchange (IE) module is employed to facilitate feature-sharing between the search and target branches. Additionally, a state-aware template update mechanism is proposed, where the template pool is dynamically updated based on their replaceability at each frame. The advantage of our proposed method lies in its utilization of spectral characteristics to refine and enhance the dimensionality-reduced image on a pixel-by-pixel basis, thereby enriching the texture of the target image. Through the IE module, it enhances the exchange of information between the target and search branches, overcoming the limitations of traditional Siamese architectures. Additionally, it employs a dynamic template update mechanism that adjusts the target template during each iteration to address the challenges posed by sudden movements of the target. Comprehensive experiments and comparisons with state-of-the-art trackers illustrate the state-of-the-art performance of the proposed approach.

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AlexNet architecture modification to classify Acute Lymphoblastic Leukemia images

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AIP Conference Proceedings 2024年第1期2952卷

作者： Arif Muntasa Rima Tri Wahyuningrum Zabrina Tuzzahra Devi Rosa Anamisa Muhammad Kautsar Sophan Computational Artificial Intelligence Laboratory Informatics Engineering Department Engineering Faculty University of Trunojoyo-Madura Ry Telang Po. Box 2 Kamal Bangkalan 69162 Indonesia Information System Computer Science Faculty UPN Veteran Jl. Raya Rungkut Madya Gunung Anyar Surabaya East Java Surabaya Indonesia Business Intelligent System Laboratory Informatics Engineering Department Engineering Faculty University of Trunojoyo-Madura Ry Telang Po. Box 2 Kamal Bangkalan 69162 Indonesia

Leukemia is a cancer disease that is very dangerous for human health. Therefore, it should be we need regularly check to know whether we are infected with Leukemia or not. Unfortunately, we need a high cost to check it early. Many researchers have proposed to detect, classify, and recognize Leukemia disease based on white blood cell images from the microscopic. This paper proposes simplifying the AlexNet architecture to classify Acute Lymphoblastic Leukemia images. We reduce the number of original AlexNet kernels from the first until the last convolution. The kernel reduction is intended to shorten the computation time while the training process. However, kernel size, number of kernels, and feature maps have impacted the number of trainable parameters. We have successfully implemented our convolutional neural network architecture to classify the Acute Lymphoblastic Leukemia image with 94.67% accuracy and 3.96 standard deviations. Our proposed architecture has delivered better results than the others, such as Naïve Bayes, Hausdrof SVM-based Leukemia Detection, and Fuzzy-based Leukemia Detection.

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Simple assembly line balancing problem type 2 by variable neighborhood strategy adaptive search: A case study garment industry

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Journal of Open Innovation: Technology, Market, and Complexity 2020年第1期6卷 21页

作者： Jirasirilerd, Ganokgarn Pitakaso, Rapeepan Sethanan, Kanchana Kaewman, Sasitorn Sirirak, Worapot Kosacka-Olejnik, Monika Department of Industrial Engineering Ubon Ratchathani University Ubon Ratchathani 34190 Thailand Research unit on System Modelling for Industry Department of Industrial Engineering Khon Kaen University Khon Kaen 40002 Thailand Department of Computer Science Mahasarakham University Mahasarakham 44000 Thailand Department of Industrial Engineering Rajamangala University of Technology Lanna Chiang Rai Chiang Rai 57120 Thailand Poznan University of Technology Poznan 61-704 Poland

This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time. © 2020 by the authors.

关键词： Cycle time Simple assembly line balancing problem type 2 Variable neighborhood strategy adaptive search

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