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The Science of the total environment 2023年 904卷 166786页

作者： Klaudia Czerwińska Anna Marszałek Edyta Kudlek Maciej Śliz Mariusz Dudziak Małgorzata Wilk Department of Heat Engineering and Environment Protection Faculty of Metals Engineering and Industrial Computer Science AGH University of Science and Technology 30 Mickiewicza Avenue 30-059 Krakow Poland. Electronic address: kczerwin@agh.edu.pl. Department of Water and Wastewater Engineering Faculty of Energy and Environmental Engineering Silesian University of Technology 18 Stanisława Konarskiego Street 44-100 Gliwice Poland. Department of Heat Engineering and Environment Protection Faculty of Metals Engineering and Industrial Computer Science AGH University of Science and Technology 30 Mickiewicza Avenue 30-059 Krakow Poland.

Epsilon Near Zero (ENZ) Based Kagome Photonic Crystal Fiber for Low Loss, High Birefringence and Near Zero Flat Dispersion in THz Regime

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Research Square

Research Square 2021年

作者： Asaduzzaman, Sayed Rehana, Hasin Aziz, M.D. Tanzil Faragallah, Osama S. Baz, Mohammed Eid, Mahmoud M.A. Rashed, Ahmed Nabih Zaki Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Rajshahi Bangladesh Dhaka Bangladesh Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical Engineering College of Engineering Taif University P.O. Box 11099 Taif21944 Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menouf 32951 Menoufia University Egypt

In this research, we have proposed two novel types of Kagome PCFs one is slotted core PCF (S-KPCF) and another one is circular PCF (C-KPCF). FEM has been applied with boundary condition to investigate the optical properties of Proposed PCFs. HRS material along with Epsilon Near Zero (ENZ) material has been applied in core and TOPAS was used as background material. Numerical investigation takes place a wider frequency range for 0.8 THz to 2 THz. Slotted core PCF (S-KPCF) shows eminent birefringence of 2.98×10-2, Lower EML of 0.232 cm-1, lower confinement loss of 2.76×10-8, Bending loss 10.76×10-9, Total loss of 0.232cm-1 at frequency 1THz. Besides circular core PCF (C-KPCF) shows eminent birefringence of 8.69×10-2, Lower effective material loss of 0.746 cm-1, lower confinement loss of 2.80×10-7, Bending loss 7.76×10-9, Total loss of 0.746cm-1 at 1THz. Both PCFs show flat dispersion near zero (for S-KPCF is -0.997ps/nm/Km and for C-KPCF is -0.797ps/nm/Km) range. V-Parameter of both of the PCF indicates that both PCF is multimode PCF. Comparison of the proposed PCFs shows that C-KPCF shows better results than S-KPCF and both PCFs shows better results than previous PCFs. © 2021, CC BY.

关键词： Birefringence

Deep Reinforcement Learning-Based Resource Scheduling Strategy for Reliability-Oriented Wireless Body Area Networks

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IEEE Sensors Letters

IEEE Sensors Letters 2021年第1期5卷 1页

作者： Xu, Yi-Han Yu, Gang Yong, Yueh-Tiam College of Information Science and Technology Nanjing Forestry University Nanjing210037 China Department of Electronic and Electrical Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Samarahan Campus Kota Samarahan94300 Malaysia

Reliability is a critical factor in designing of wireless body area networks. In this letter, we propose a resource scheduling strategy and solving an optimization problem to maximize the reliability of the transmission of emergency-critical sensory data. We jointly consider transmission mode, relay selection, time slot allocation, and transmit power of each body sensor and formulating the scheduling problem to be a Markov decision process. In this strategy, the scheduling decision is made by each body sensor that do not have complete and global network information. Owning to the formulated problem is nonconvex and the high computation complexity, we propose a deep reinforcement learning algorithm to solve the problem. Numerical results reveal that the proposed strategy is capacity of guaranteeing the reliability of transmission with an acceptable convergence speed. © 2017 IEEE.

关键词： Reinforcement learning

CLS-Miner: efficient and effective closed high-utility itemset mining

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Frontiers of computer Science 2019年第2期13卷 357-381页

作者： Thu-Lan DAM Kenli LI Philippe FOURNIER-VIGER Quang-Huy DUONG College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Faculty of Information Technology Hanoi University of Industry Hanoi Vietnam CIC of HPC National University of Defense Technology Changsha 410073 China National Supercomputing Center in Changsha Changsha 410082 China School of Natural Sciences and Humanities Harbin Institute of Technology Shenzhen Graduate School Shenzhen 518055 China Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

High-utility itemset mining (HUIM) is a popular data mining task with applications in numerous domains. However, traditional HUIM algorithms often produce a very large set of high-utility itemsets (HUIs). As a result, analyzing HUIs can be very time consuming for users. Moreover, a large set of HUIs also makes HUIM algorithms less efficient in terms of execution time and memory consumption. To address this problem, closed high-utility itemsets (CHUIs), concise and lossless representations of all HUIs, were proposed recently. Although mining CHUIs is useful and desirable, it remains a computationally expensive task. This is because current algorithms often generate a huge number of candidate itemsets and are unable to prune the search space effectively. In this paper, we address these issues by proposing a novel algorithm called CLS-Miner. The proposed algorithm utilizes the utility-list structure to directly compute the utilities of itemsets without producing candidates. It also introduces three novel strategies to reduce the search space, namely chain-estimated utility co-occurrence pruning, lower branch pruning, and pruning by coverage. Moreover, an effective method for checking whether an itemset is a subset of another itemset is introduced to further reduce the time required for discovering CHUIs. To evaluate the performance of the proposed algorithm and its novel strategies, extensive experiments have been conducted on six benchmark datasets having various characteristics. Results show that the proposed strategies are highly efficient and effective, that the proposed CLS-Miner algorithm outperforms the current state-ofthe- art CHUD and CHUI-Miner algorithms, and that CLSMiner scales linearly.

关键词： utility mining high-utility itemset mining closed itemset mining closed high-utility itemset mining

A Study on Security Awareness in Mobile Devices 1

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A Study on Security Awareness in Mobile Devices

1st International Informatics and Software engineering Conference, IISEC 2019

作者： Zeybek, Mine Yilmaz, Ercan Nurcan Dogru, Ibrahim Alper Gazi University Information Security Engineering Institute of Natural and Applied Sciences Ankara06500 Turkey Gazi University Electrical and Electronic Engineering Faculty of Technology Ankara06500 Turkey Gazi University Computer Engineering Faculty of Technology Ankara06500 Turkey

ISBN: (纸本)9781728139920

With life becoming mobile in recent years, the use of smartphones is rapidly increasing in daily and business life. The increase in the use of mobile devices has made it increasingly difficult to ensure their security and privacy. Some of the attacks on mobile devices are related to social engineering. The main way to prevent such attacks is user awareness. In this study;A sample field study was conducted on the awareness of public institution personnel about the use of mobile devices. A questionnaire was developed and applied to 120 qualified public employees. Evaluations were made regarding the answers given in the questionnaire. The controls that can be evaluated to be included in the IT audit are specified and recommendations are made for the protection of mobile devices. © 2019 IEEE.

关键词： Cybersecurity

Early Detection of Covid-19 Disease using Computed Tomography Images and Optimized CNN-LSTM

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Early Detection of Covid-19 Disease using Computed Tomograph...

International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Muhammad Hammad Memon Noorbakhsh Amiri Golilarz Jianping Li Mohammad Yazdi Abdoljalil Addeh School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Instituto Superior Técnico University of Lisbon Lisbon Portugal Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

ISBN: (数字)9781665405058

ISBN: (纸本)9781665405065

Since the novel Coronavirus (COVID-19) pandemic showed up in China, it became a big problem for health authorities to counter this life-threatening disease. Early light signs such as fever and nonproductive cough give a chance for early detection of disease and appropriate treatment. Imaging features that can be obtained using computed tomography (CT) images are of the most significant aspects of COVID-19 for screening, examination, therapy evaluation, and follow-up. This paper proposes an intelligent method for early detection of COVID-19 based on CT images and deep neural networks. In the developed method, the convolutional neural network (CNN) is used for automatic feature extraction from CT images and long-short term memory (LSTM) is used for final classification. Moreover, the Harris hawk optimization (HHO) algorithm is implemented for finding the best possible value of internal parameters of CNN and LSTM, such as the number of convolution/pooling layers, size, and the number of convolution kernels with the aim of increasing the classification accuracy. The developed method tested on data collected in Mashi Daneshvari Hospital in Iran. The obtained results showed that the developed method could detect the COVID-19 with high accuracy without needing radiologist experts.

关键词： COVID-19 Computed tomography Feature extraction Diseases Lung Classification algorithms Optimization

AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars

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arXiv 2021年

作者： Lyu, Bo Wang, Shengbo Wen, Shiping Shi, Kaibo Yang, Yin Zeng, Lingfang Huang, Tingwen Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar Science Program Texas A & M University at Qatar Doha23874 Qatar

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional computing architectures (CPU, GPU, or TPU). But the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption, to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static, and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model (LSTM, combined with the dynamic-fill scheme) generates remarkable mapping performance on a small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms. Copyright © 2021, The Authors. All rights reserved.

关键词： Memory architecture

Integration of TinyML and LargeML: A Survey of 6G and Beyond

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arXiv 2025年

作者： Vu, Thai-Hoc Tu, Ngo Hoang Huynh-The, Thien Lee, Kyungchun Kim, Sunghwan Voznak, Miroslav Pham, Quoc-Viet School of Electronic Engineering Kyonggi University Korea Republic of Department of Electrical and Information Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Department of Computer and Communications Engineering Ho Chi Minh City University of Technology and Education Viet Nam Department of Electrical and Information Engineering the Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul01811 Korea Republic of Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. Listopadu 2172/15 Ostrava708 00 Czech Republic School of Computer Science and Statistics Trinity College Dublin Dublin2 D02 PN40 Ireland

The transition from 5G networks to 6G highlights a significant demand for machine learning (ML). Deep learning models, in particular, have seen wide application in mobile networking and communications to support advanced services in emerging wireless environments, such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. The rapid expansion of Internet-of-Things (IoT) devices, many with limited computational capabilities, has accelerated the development of tiny machine learning (TinyML) and resource-efficient ML approaches for cost-effective services. However, the deployment of large-scale machine learning (LargeML) solutions require major computing resources and complex management strategies to support extensive IoT services and ML-generated content applications. Consequently, the integration of TinyML and LargeML is projected as a promising approach for future seamless connectivity and efficient resource management. Although the integration of TinyML and LargeML shows abundant potential, several challenges persist, including performance optimization, practical deployment strategies, effective resource management, and security considerations. In this survey, we review and analyze the latest research aimed at enabling the integration of TinyML and LargeML models for the realization of smart services and applications in future 6G networks and beyond. The paper concludes by outlining critical challenges and identifying future research directions for the holistic integration of TinyML and LargeML in next-generation wireless networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

Full-Resilient Memory-Optimum Multi-Party Non-Interactive Key Exchange

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arXiv 2021年

作者： Salimi, Majid Mala, Hamid Martin, Honorio Peris-Popez, Pedro Faculty of Computer Engineering University of Isfahan Isfahan Iran Department of Electronic Technology University Carlos III of Madrid Avda. de la Universidad 30 Leganés28911 Spain Department of Computer Science University Carlos III of Madrid Avda. de la Universidad 30 Leganés28911 Spain

Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental cryptographic primitive in which users register into a key generation centre and receive a public/private key pair each. After that, any subset of these users can compute a shared key without any interaction. Nowadays, IoT devices suffer from a high number and large size of messages exchanged in the Key Management Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate the airtime and latency of messages transferred between IoT devices. MP-NIKE schemes can be realized by using multilinear maps. There are several attempts for constructing multilinear maps based on indistinguishable obfuscation, lattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes are inefficient in terms of computation cost and memory overhead. Besides, several attacks have been recently reported against CRT-based and lattice-based multilinear maps. There is only one modular exponentiation-based MP-NIKE scheme in the literature which has been claimed to be both secure and efficient. In this article, we present an attack on this scheme based on the Euclidean algorithm, in which two colluding users can obtain the shared key of any arbitrary subgroup of users. We also propose an efficient and secure MP-NIKE scheme. We show how our proposal is secure in the random oracle model assuming the hardness of the root extraction modulo a composite number. © 2021, CC BY.

关键词： Internet of things

Adapting to dynamic LEO-B5G systems: Meta-critic learning based efficient resource scheduling

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arXiv 2021年

作者： Yuan, Yaxiong Lei, Lei Vu, Thang X. Chang, Zheng Chatzinotas, Symeon Sun, Sumei The Interdisciplinary Centre for Security Reliability and Trust Luxembourg University Kirchberg1855 Luxembourg The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China The Faculty of Information Technology University of Jyväskylä JyväskyläFI-40014 Finland The Institute for Infocomm Research Agency for Science Technology and Research Singapore138632 Singapore

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. To address them, we first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL's effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning